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BIOTECHNOLOGY (237 journals)                  1 2 | Last

Showing 1 - 200 of 237 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 8)
Advanced Biomedical Research     Open Access  
Advances in Bioscience and Biotechnology     Open Access   (Followers: 14)
Advances in Genetic Engineering & Biotechnology     Hybrid Journal   (Followers: 8)
African Journal of Biotechnology     Open Access   (Followers: 6)
Algal Research     Partially Free   (Followers: 10)
American Journal of Biochemistry and Biotechnology     Open Access   (Followers: 64)
American Journal of Bioinformatics Research     Open Access   (Followers: 7)
American Journal of Polymer Science     Open Access   (Followers: 31)
Anadolu University Journal of Science and Technology : C Life Sciences and Biotechnology     Open Access  
Animal Biotechnology     Hybrid Journal   (Followers: 8)
Annales des Sciences Agronomiques     Full-text available via subscription  
Applied Biochemistry and Biotechnology     Hybrid Journal   (Followers: 43)
Applied Bioenergy     Open Access  
Applied Biosafety     Hybrid Journal  
Applied Food Biotechnology     Open Access   (Followers: 3)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 63)
Applied Mycology and Biotechnology     Full-text available via subscription   (Followers: 4)
Arthroplasty Today     Open Access   (Followers: 1)
Artificial Cells, Nanomedicine and Biotechnology     Hybrid Journal   (Followers: 1)
Asia Pacific Biotech News     Hybrid Journal   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Pacific Journal of Tropical Biomedicine     Open Access   (Followers: 2)
Australasian Biotechnology     Full-text available via subscription   (Followers: 1)
Banat's Journal of Biotechnology     Open Access  
BBR : Biochemistry and Biotechnology Reports     Open Access   (Followers: 5)
Bio-Algorithms and Med-Systems     Hybrid Journal   (Followers: 2)
Bio-Research     Full-text available via subscription   (Followers: 2)
Bioactive Materials     Open Access   (Followers: 1)
Biocatalysis and Agricultural Biotechnology     Hybrid Journal   (Followers: 4)
Biocybernetics and Biological Engineering     Full-text available via subscription   (Followers: 5)
Bioethics UPdate     Hybrid Journal  
Biofuels     Hybrid Journal   (Followers: 11)
Biofuels Engineering     Open Access   (Followers: 1)
Biological & Pharmaceutical Bulletin     Full-text available via subscription   (Followers: 4)
Biological Cybernetics     Hybrid Journal   (Followers: 10)
Biomarkers and Genomic Medicine     Open Access   (Followers: 3)
Biomarkers in Drug Development     Partially Free   (Followers: 1)
Biomaterials Research     Open Access   (Followers: 4)
BioMed Research International     Open Access   (Followers: 4)
Biomédica     Open Access  
Biomedical and Biotechnology Research Journal     Open Access  
Biomedical Engineering Research     Open Access   (Followers: 6)
Biomedical glasses     Open Access  
Biomedical Reports     Full-text available via subscription  
BioMedicine     Open Access  
Biomedika     Open Access  
Bioprinting     Hybrid Journal   (Followers: 1)
Bioresource Technology Reports     Hybrid Journal   (Followers: 1)
Bioscience, Biotechnology, and Biochemistry     Hybrid Journal   (Followers: 21)
Biosimilars     Open Access   (Followers: 1)
Biosurface and Biotribology     Open Access  
Biotechnic and Histochemistry     Hybrid Journal   (Followers: 2)
BioTechniques : The International Journal of Life Science Methods     Full-text available via subscription   (Followers: 28)
Biotechnologia Acta     Open Access   (Followers: 1)
Biotechnologie, Agronomie, Société et Environnement     Open Access   (Followers: 2)
Biotechnology     Open Access   (Followers: 5)
Biotechnology & Biotechnological Equipment     Open Access   (Followers: 4)
Biotechnology Advances     Hybrid Journal   (Followers: 33)
Biotechnology and Applied Biochemistry     Hybrid Journal   (Followers: 44)
Biotechnology and Bioengineering     Hybrid Journal   (Followers: 155)
Biotechnology and Bioprocess Engineering     Hybrid Journal   (Followers: 5)
Biotechnology and Genetic Engineering Reviews     Hybrid Journal   (Followers: 13)
Biotechnology and Health Sciences     Open Access   (Followers: 1)
Biotechnology and Molecular Biology Reviews     Open Access   (Followers: 1)
Biotechnology Annual Review     Full-text available via subscription   (Followers: 5)
Biotechnology for Biofuels     Open Access   (Followers: 10)
Biotechnology Frontier     Open Access   (Followers: 2)
Biotechnology Journal     Hybrid Journal   (Followers: 16)
Biotechnology Law Report     Hybrid Journal   (Followers: 4)
Biotechnology Letters     Hybrid Journal   (Followers: 34)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Biotechnology Reports     Open Access  
Biotechnology Research International     Open Access   (Followers: 1)
Biotechnology Techniques     Hybrid Journal   (Followers: 10)
Biotecnología Aplicada     Open Access  
Bioteknologi (Biotechnological Studies)     Open Access  
Biotribology     Hybrid Journal   (Followers: 1)
BMC Biotechnology     Open Access   (Followers: 16)
Cell Biology and Development     Open Access  
Chinese Journal of Agricultural Biotechnology     Full-text available via subscription   (Followers: 4)
Communications in Mathematical Biology and Neuroscience     Open Access  
Computational and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computer Methods and Programs in Biomedicine     Hybrid Journal   (Followers: 8)
Contributions to Tobacco Research     Open Access   (Followers: 2)
Copernican Letters     Open Access   (Followers: 1)
Critical Reviews in Biotechnology     Hybrid Journal   (Followers: 20)
Crop Breeding and Applied Biotechnology     Open Access   (Followers: 3)
Current Bionanotechnology     Hybrid Journal  
Current Biotechnology     Hybrid Journal   (Followers: 4)
Current Opinion in Biomedical Engineering     Hybrid Journal   (Followers: 1)
Current Opinion in Biotechnology     Hybrid Journal   (Followers: 56)
Current Pharmaceutical Biotechnology     Hybrid Journal   (Followers: 9)
Current Research in Bioinformatics     Open Access   (Followers: 12)
Current Trends in Biotechnology and Chemical Research     Open Access   (Followers: 3)
Current trends in Biotechnology and Pharmacy     Open Access   (Followers: 8)
EBioMedicine     Open Access  
Electronic Journal of Biotechnology     Open Access  
Entomologia Generalis     Full-text available via subscription  
Environmental Science : Processes & Impacts     Full-text available via subscription   (Followers: 4)
Experimental Biology and Medicine     Hybrid Journal   (Followers: 3)
Folia Medica Indonesiana     Open Access  
Food Bioscience     Hybrid Journal  
Food Biotechnology     Hybrid Journal   (Followers: 9)
Food Science and Biotechnology     Hybrid Journal   (Followers: 8)
Frontiers in Bioengineering and Biotechnology     Open Access   (Followers: 6)
Frontiers in Systems Biology     Open Access   (Followers: 2)
Fungal Biology and Biotechnology     Open Access   (Followers: 2)
GM Crops and Food: Biotechnology in Agriculture and the Food Chain     Full-text available via subscription   (Followers: 1)
GSTF Journal of BioSciences     Open Access  
HAYATI Journal of Biosciences     Open Access  
Horticulture, Environment, and Biotechnology     Hybrid Journal   (Followers: 11)
IEEE Transactions on Molecular, Biological and Multi-Scale Communications     Hybrid Journal   (Followers: 1)
IET Nanobiotechnology     Hybrid Journal   (Followers: 2)
IIOAB Letters     Open Access  
IN VIVO     Full-text available via subscription   (Followers: 4)
Indian Journal of Biotechnology (IJBT)     Open Access   (Followers: 2)
Indonesia Journal of Biomedical Science     Open Access   (Followers: 2)
Indonesian Journal of Biotechnology     Open Access   (Followers: 1)
Industrial Biotechnology     Hybrid Journal   (Followers: 18)
International Biomechanics     Open Access  
International Journal of Bioinformatics Research and Applications     Hybrid Journal   (Followers: 13)
International Journal of Biomechatronics and Biomedical Robotics     Hybrid Journal   (Followers: 4)
International Journal of Biomedical Research     Open Access   (Followers: 2)
International Journal of Biotechnology     Hybrid Journal   (Followers: 5)
International Journal of Biotechnology and Molecular Biology Research     Open Access   (Followers: 2)
International Journal of Biotechnology for Wellness Industries     Partially Free   (Followers: 1)
International Journal of Environment, Agriculture and Biotechnology     Open Access   (Followers: 5)
International Journal of Functional Informatics and Personalised Medicine     Hybrid Journal   (Followers: 4)
International Journal of Medicine and Biomedical Research     Open Access   (Followers: 1)
International Journal of Nanotechnology and Molecular Computation     Full-text available via subscription   (Followers: 3)
International Journal of Radiation Biology     Hybrid Journal   (Followers: 4)
Iranian Journal of Biotechnology     Open Access  
ISABB Journal of Biotechnology and Bioinformatics     Open Access  
Italian Journal of Food Science     Open Access   (Followers: 1)
Journal of Biometrics & Biostatistics     Open Access   (Followers: 3)
Journal of Bioterrorism & Biodefense     Open Access   (Followers: 6)
Journal of Petroleum & Environmental Biotechnology     Open Access   (Followers: 1)
Journal of Advanced Therapies and Medical Innovation Sciences     Open Access  
Journal of Advances in Biotechnology     Open Access   (Followers: 5)
Journal Of Agrobiotechnology     Open Access  
Journal of Analytical & Bioanalytical Techniques     Open Access   (Followers: 7)
Journal of Animal Science and Biotechnology     Open Access   (Followers: 4)
Journal of Applied Biomedicine     Open Access   (Followers: 2)
Journal of Applied Biotechnology     Open Access   (Followers: 2)
Journal of Applied Biotechnology Reports     Open Access   (Followers: 2)
Journal of Applied Mathematics & Bioinformatics     Open Access   (Followers: 5)
Journal of Biologically Active Products from Nature     Hybrid Journal   (Followers: 1)
Journal of Biomaterials and Nanobiotechnology     Open Access   (Followers: 6)
Journal of Biomedical Photonics & Engineering     Open Access  
Journal of Biomedical Practitioners     Open Access  
Journal of Bioprocess Engineering and Biorefinery     Full-text available via subscription  
Journal of Bioprocessing & Biotechniques     Open Access  
Journal of Biosecurity, Biosafety and Biodefense Law     Hybrid Journal   (Followers: 3)
Journal of Biotechnology     Hybrid Journal   (Followers: 68)
Journal of Biotechnology and Strategic Health Research     Open Access  
Journal of Chemical and Biological Interfaces     Full-text available via subscription   (Followers: 1)
Journal of Chemical Technology & Biotechnology     Hybrid Journal   (Followers: 9)
Journal of Chitin and Chitosan Science     Full-text available via subscription  
Journal of Colloid Science and Biotechnology     Full-text available via subscription  
Journal of Commercial Biotechnology     Full-text available via subscription   (Followers: 6)
Journal of Crop Science and Biotechnology     Hybrid Journal   (Followers: 3)
Journal of Essential Oil Research     Hybrid Journal   (Followers: 2)
Journal of Experimental Biology     Full-text available via subscription   (Followers: 24)
Journal of Genetic Engineering and Biotechnology     Open Access   (Followers: 5)
Journal of Ginseng Research     Open Access  
Journal of Industrial Microbiology and Biotechnology     Hybrid Journal   (Followers: 16)
Journal of Integrative Bioinformatics     Open Access  
Journal of International Biotechnology Law     Hybrid Journal   (Followers: 3)
Journal of Medical Imaging and Health Informatics     Full-text available via subscription  
Journal of Molecular Biology and Biotechnology     Open Access  
Journal of Molecular Microbiology and Biotechnology     Full-text available via subscription   (Followers: 11)
Journal of Nano Education     Full-text available via subscription  
Journal of Nanobiotechnology     Open Access   (Followers: 4)
Journal of Nanofluids     Full-text available via subscription   (Followers: 1)
Journal of Organic and Biomolecular Simulations     Open Access  
Journal of Plant Biochemistry and Biotechnology     Hybrid Journal   (Followers: 4)
Journal of Science and Applications : Biomedicine     Open Access  
Journal of the Mechanical Behavior of Biomedical Materials     Hybrid Journal   (Followers: 11)
Journal of Trace Elements in Medicine and Biology     Hybrid Journal   (Followers: 1)
Journal of Tropical Microbiology and Biotechnology     Full-text available via subscription  
Journal of Yeast and Fungal Research     Open Access   (Followers: 1)
Marine Biotechnology     Hybrid Journal   (Followers: 4)
Messenger     Full-text available via subscription  
Metabolic Engineering Communications     Open Access   (Followers: 4)
Metalloproteinases In Medicine     Open Access  
Microalgae Biotechnology     Open Access   (Followers: 2)
Microbial Biotechnology     Open Access   (Followers: 9)
MicroMedicine     Open Access   (Followers: 3)
Molecular and Cellular Biomedical Sciences     Open Access  
Molecular Biotechnology     Hybrid Journal   (Followers: 13)
Molecular Genetics and Metabolism Reports     Open Access   (Followers: 3)
Nanobiomedicine     Open Access  
Nanobiotechnology     Hybrid Journal   (Followers: 2)
Nanomaterials and Nanotechnology     Open Access  
Nanomaterials and Tissue Regeneration     Open Access  
Nanomedicine and Nanobiology     Full-text available via subscription  
Nanomedicine Research Journal     Open Access  
Nanotechnology Reviews     Hybrid Journal   (Followers: 5)
Nature Biotechnology     Full-text available via subscription   (Followers: 535)

        1 2 | Last

Journal Cover
Computer Methods and Programs in Biomedicine
Journal Prestige (SJR): 0.786
Citation Impact (citeScore): 3
Number of Followers: 8  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0169-2607
Published by Elsevier Homepage  [3163 journals]
  • New approaches to obtaining individual peak height velocity and age at
           peak height velocity from the SITAR model
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Zhiqiang Cao, L.L. Hui, M.Y. Wong
      Objective We compared three methods for estimating the individual peak height velocity (PHV) and age at peak height velocity (APHV) from the SuperImposition by Translation and Rotation (SITAR) model. Methods We fitted the SITAR model using simulated data and heights of 12 girls from the Chard Growth Study and obtained individual PHVs and APHVs from three methods: the model method, the quadratic function method and the numerical method, which are available in our newly developed R package“iapvbs”. The mean, interquartile range, range of biases in estimated APHV and PHV as well as the rates of warning and unreasonable cases, i.e. estimated APHVs being outside the range of age measurements, from the three methods were presented and compared. Results When the growth curves of all individuals were well fitted by the SITAR model, all three methods estimated individual APHVs with similarly small biases, with a few unreasonable cases (0.16%) observed when the model method was used while more computation time required for the numerical method. When the growth curves of some individuals were not very well fitted, the model method generated more unreasonable individual APHV (8.15%) and more bias in PHV and APHV, compared to those estimated by the numerical method and quadratic function method. In line with the observations from the simulated data, the real data analysis demonstrated that the numerical method generated more reliable PHV and APHV for individuals with growth curve not well fitted by the SITAR model. Conclusion The performance of different methods estimating individual APHV depends largely on how well the growth curves are fitted by the SITAR model. The quadratic function method is more superior when growth curves of all individuals are well fitted by the SITAR model; otherwise, the numerical method should be adopted for getting most robust estimates of PHV and APHV. The model method generates unreasonable APHV estimates, particularly when the growth curves are not well fitted.

      PubDate: 2018-06-19T10:47:58Z
  • Fetal health status prediction based on maternal clinical history using
           machine learning techniques
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Akhan Akbulut, Egemen Ertugrul, Varol Topcu
      Background and Objective Congenital anomalies are seen at 1–3% of the population, probabilities of which are tried to be found out primarily through double, triple and quad tests during pregnancy. Also, ultrasonographical evaluations of fetuses enhance detecting and defining these abnormalities. About 60–70% of the anomalies can be diagnosed via ultrasonography, while the remaining 30–40% can be diagnosed after childbirth. Medical diagnosis and prediction is a topic that is closely related with e-Health and machine learning. e-Health applications are critically important especially for the patients unable to see a doctor or any health professional. Our objective is to help clinicians and families to better predict fetal congenital anomalies besides the traditional pregnancy tests using machine learning techniques and e-Health applications. Methods In this work, we developed a prediction system with assistive e-Health applications which both the pregnant women and practitioners can make use of. A performance comparison (considering Accuracy, F1-Score, AUC measures) was made between 9 binary classification models (Averaged Perceptron, Boosted Decision Tree, Bayes Point Machine, Decision Forest, Decision Jungle, Locally-Deep Support Vector Machine, Logistic Regression, Neural Network, Support Vector Machine) which were trained with the clinical dataset of 96 pregnant women and used to process data to predict fetal anomaly status based on the maternal and clinical data. The dataset was obtained through maternal questionnaire and detailed evaluations of 3 clinicians from RadyoEmar radiodiagnostics center in Istanbul, Turkey. Our e-Health applications are used to get pregnant women’s health status and clinical history parameters as inputs, recommend them physical activities to perform during pregnancy, and inform the practitioners and finally the patients about possible risks of fetal anomalies as the output. Results In this paper, the highest accuracy of prediction was displayed as 89.5% during the development tests with Decision Forest model. In real life testing with 16 users, the performance was 87.5%. This estimate is sufficient to give an idea of fetal health before the patient visits the physician. Conclusions The proposed work aims to provide assistive services to pregnant women and clinicians via an online system consisting of a mobile side for the patients, a web application side for their clinicians and a prediction system. In addition, we showed the impact of certain clinical data parameters of pregnant on the fetal health status, statistically correlated the parameters with the existence of fetal anomalies and showed guidelines for future researches.
      Graphical abstract image

      PubDate: 2018-06-19T10:47:58Z
  • Magnetohydrodynamic blood flow in patients with coronary artery disease
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Ashkan Javadzadegan, Abouzar Moshfegh, Hamid Hassanzadeh Afrouzi, Mohammad Omidi
      Objectives We aim to investigate the effect of a magnetic field with varying intensities on haemodynamic perturbations in a cohort of patients with coronary artery disease. Methods Transient computational fluid dynamics (CFD) simulations were performed in three-dimensional (3D) models of coronary arteries reconstructed from 3D quantitative coronary angiography. The effect of magnetic field on wall shear stress (WSS) derived parameters including maximum wall shear stress (MWSS) and size of regions with low wall shear stress (ALWSS) as well as length of flow recirculation zones were determined. Results The results showed a substantial reduction in MWSS, ALWSS and length of flow recirculation zones in the presence of magnetic field, in particular for coronaries with moderate to severe stenoses. When the whole cohort examined, time-averaged wall shear stress (TAWSS), ALWSS and the length of flow recirculation zones in the absence of magnetic field were approximately 1.71, 4.69 and 8.46 times greater than those in the presence of magnetic field, respectively. Conclusion Our findings imply that an externally applied magnetic field can improve haemodynamic perturbations in human coronary arteries.

      PubDate: 2018-06-19T10:47:58Z
  • Lymphoma images analysis using morphological and non-morphological
           descriptors for classification
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Marcelo Zanchetta do Nascimento, Alessandro Santana Martins, Thaína Aparecida Azevedo Tosta, Leandro Alves Neves
      Mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia are the principle subtypes of the non-Hodgkin lymphomas. The diversity of clinical presentations and the histopathological features have made diagnosis of such lymphomas a complex task for specialists. Computer aided diagnosis systems employ computational vision and image processing techniques, which contribute toward the detection, diagnosis and prognosis of digitised images of histological samples. Studies aimed at improving the understanding of morphological and non-morphological features for classification of lymphoma remain a challenge in this area. This work presents a new approach for the classification of information extracted from morphological and non-morphological features of nuclei of lymphoma images. We extracted data of the RGB model of the image and employed contrast limited adaptive histogram equalisation and 2D order-statistics filter methods to enhance the contrast and remove noise. The regions of interest were identified by the global thresholding method. The flood-fill and watershed techniques were used to remove the small false positive regions. The area, extent, perimeter, convex area, solidity, eccentricity, equivalent diameter, minor axis and major axis measurements were computed for the regions detected in the nuclei. In the feature selection stage, we applied the ANOVA, Ansari-Bradley and Wilcoxon rank sum methods. Finally, we employed the polynomial, support vector machine, random forest and decision tree classifiers in order to evaluate the performance of the proposed approach. The non-morphological features achieved higher AUC and AC values for all cases: the obtained rates were between 95% and 100%. These results are relevant, especially when we consider the difficulties of clinical practice in distinguishing the studied groups. The proposed approach is useful as an automated protocol for the diagnosis of lymphoma histological tissue.

      PubDate: 2018-06-19T10:47:58Z
  • Hybrid constraint optimization for 3D subcutaneous vein reconstruction by
           near-infrared images
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Chan Wu, Jian Yang, Jianjun Zhu, Weijian Cong, Danni Ai, Hong Song, Xiaohui Liang, Yongtian Wang
      Background and objective The development of biometric identification technology and intelligent medication has enabled researchers to analyze subcutaneous veins from near-infrared images. However, the stereo reconstruction of subcutaneous veins has not been well studied, and the results are difficult to utilize in clinical practice. Methods We present a hybrid constraint optimization (HCO) matching algorithm for vein reconstruction to solve the matching failure problems caused by the incomplete segmentation of vein structures captured from different views. This algorithm initially introduces the existence of the epipolar and homography constraints in the subcutaneous vein matching. Then, the HCO matching algorithm of the vascular centerline is established by homography point-to-point matching, homography matrix optimization, and vascular section matching. Finally, the 3D subcutaneous vein is reconstructed on the basis of the principle of triangulation and system calibration parameters. Results To validate the performance of the proposed matching method, we designed a series of experiments to evaluate the effectiveness of the hybrid constraint optimization method. The experiments were performed on simulated and real datasets. 42 real vascular images were analyzed on the basis of different matching strategies. Experimental result shows that the matching accuracy increased significantly with the proposed optimization matching method. To calculate the reconstruction accuracy, we reconstructed seven simulated cardboards and measured 10 vascular distances in each simulated cardboard. The average vascular distance error of each simulated image was within 1.0 mm, and the distance errors of 75% feature points were less than 1.5 mm. Also, we printed a 3D simulated vein model to improve the illustration of this system. The reconstruction error extends from −3.58 mm to 1.94 mm with a standard deviation of 0.68 mm and a mean of 0.07 mm. Conclusions The algorithm is validated in terms of homography optimization, matching efficiency, and simulated vascular reconstruction error. The experimental results demonstrate that the veins captured from the left and right views can be accurately matched through the proposed algorithm.

      PubDate: 2018-06-19T10:47:58Z
  • Improving the quality healthcare through the efficient computer-aided
           prediction models
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Phung-Anh (Alex) Nguyen, Yu-Chuan (Jack) Li

      PubDate: 2018-06-19T10:47:58Z
  • A new deformable model based on fractional Wright energy function for
           tumor segmentation of volumetric brain MRI scans
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Rabha W. Ibrahim, Ali M. Hasan, Hamid A. Jalab
      Background and objectives The MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features’ intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficulty that encounters the active contour segmentation is the boundary tracking which is controlled by minimization of energy function for segmentation. Hence, this study proposes a novel fractional Wright function (FWF) as a minimization of energy technique to improve the performance of active contour without edge method. Method In this study, we implement FWF as an energy minimization function to replace the standard gradient-descent method as minimization function in Chan–Vese segmentation technique. The proposed FWF is used to find the boundaries of an object by controlling the inside and outside values of the contour. In this study, the objective evaluation is used to distinguish the differences between the processed segmented images and ground truth using a set of statistical parameters; true positive, true negative, false positive, and false negative. Results The FWF as a minimization of energy was successfully implemented on BRATS 2013 image dataset. The achieved overall average sensitivity score of the brain tumors segmentation was 94.8 ± 4.7%. Conclusions The results demonstrate that the proposed FWF method minimized the energy function more than the gradient-decent method that was used in the original three-dimensional active contour without edge (3DACWE) method.

      PubDate: 2018-06-06T10:22:59Z
  • Classification of lung cancer subtypes based on autofluorescence
           bronchoscopic pattern recognition: A preliminary study
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Po-Hao Feng, Tzu-Tao Chen, Yin-Tzu Lin, Shang-Yu Chiang, Chung-Ming Lo
      Background and objectives Lung cancer is the leading cause of cancer deaths worldwide. With current use of autofluorescent bronchoscopic imaging to detect early lung cancer and limitations of pathologic examinations, a computer-aided diagnosis (CAD) system based on autofluorescent bronchoscopy was proposed to distinguish different pathological cancer types to achieve objective and consistent diagnoses. Methods The collected database consisted of 12 adenocarcinomas and 11 squamous cell carcinomas. The corresponding autofluorescent bronchoscopic images were first transformed to a hue (H), saturation (S), and value (V) color space to obtain better interpretation of the color information. Color textural features were respectively extracted from the H, S, and V channels and combined in a logistic regression classifier to classify malignant types by machine learning. Results After feature selection, the proposed CAD system achieved an accuracy of 83% (19/23), a sensitivity of 73% (8/11), a specificity of 92% (11/12), a positive predictive value of 89% (8/9), a negative predictive value of 79% (11/14), and an area under the receiver operating characteristic curve of 0.81 for distinguishing lung cancer types. Conclusions The proposed CAD system based on color textures of autofluorescent bronchoscopic images provides a diagnostic method of malignant types in clinical use.

      PubDate: 2018-06-06T10:22:59Z
  • A hackathon promoting Taiwanese health-IoT innovation
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Usman Iqbal, Alon Dagan, Shabbir Syed-Abdul, Leo Anthony Celi, Min-Huei Hsu, Yu-Chuan Jack Li

      PubDate: 2018-06-06T10:22:59Z
  • Automatic macular edema identification and characterization using OCT
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Gabriela Samagaio, Aída Estévez, Joaquim de Moura, Jorge Novo, María Isabel Fernández, Marcos Ortega
      Background and objective The detection and characterization of the intraretinal fluid accumulation constitutes a crucial ophthalmological issue as it provides useful information for the identification and diagnosis of the different types of Macular Edema (ME). These types are clinically defined, according to the clinical guidelines, as: Serous Retinal Detachment (SRD), Diffuse Retinal Thickening (DRT) and Cystoid Macular Edema (CME). Their accurate identification and characterization facilitate the diagnostic process, determining the disease severity and, therefore, allowing the clinicians to achieve more precise analysis and suitable treatments. Methods This paper proposes a new fully automatic system for the identification and characterization of the three types of ME using Optical Coherence Tomography (OCT) images. In the case of SRD and CME edemas, multilevel image thresholding approaches were designed and combined with the application of ad-hoc clinical restrictions. The case of DRT edemas, given their complexity and fuzzy regional appearance, was approached by a learning strategy that exploits intensity, texture and clinical-based information to identify their presence. Results The system provided satisfactory results with F-Measures of 87.54% and 91.99% for the DRT and CME detections, respectively. In the case of SRD edemas, the system correctly detected all the cases that were included in the designed dataset. Conclusions The proposed methodology offered an accurate performance for the individual identification and characterization of the three different types of ME in OCT images. In fact, the method is capable to handle the ME analysis even in cases of significant severity with the simultaneous existence of the three ME types that appear merged inside the retinal layers.
      Graphical abstract image

      PubDate: 2018-06-06T10:22:59Z
  • Radiomics based detection and characterization of suspicious lesions on
           full field digital mammograms
    • Abstract: Publication date: September 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 163
      Author(s): Suhas G. Sapate, Abhishek Mahajan, Sanjay N. Talbar, Nilesh Sable, Subhash Desai, Meenakshi Thakur
      Background and objective Early detection is the important key to reduce breast cancer mortality rate. Detecting the mammographic abnormality as a subtle sign of breast cancer is essential for the proper diagnosis and treatment. The aim of this preliminary study is to develop algorithms which detect suspicious lesions and characterize them to reduce the diagnostic errors regarding false positives and false negatives. Methods The proposed hybrid mechanism detects suspicious lesions automatically using connected component labeling and adaptive fuzzy region growing algorithm. A novel neighboring pixel selection algorithm reduces the computational complexity of the seeded region growing algorithm used to finalize lesion contours. These lesions are characterized using radiomic features and then classified as benign mass or malignant tumor using k-NN and SVM classifiers. Two datasets of 460 full field digital mammograms (FFDM) utilized in this clinical study consists of 210 images with malignant tumors, 30 with benign masses and 220 normal breast images that are validated by radiologists expert in mammography. Results The qualitative assessment of segmentation results by the expert radiologists shows 91.67% sensitivity and 58.33% specificity. The effects of seven geometric and 48 textural features on classification accuracy, false positives per image (FPsI), sensitivity and specificity are studied separately and together. The features together achieved the sensitivity of 84.44% and 85.56%, specificity of 91.11% and 91.67% with FPsI of 0.54 and 0.55 using k-NN and SVM classifiers respectively on local dataset. Conclusions The overall breast cancer detection performance of proposed scheme after combining geometric and textural features with both classifiers is improved in terms of sensitivity, specificity, and FPsI.

      PubDate: 2018-06-03T10:15:11Z
  • A decision support system for type 1 diabetes mellitus diagnostics based
           on dual channel analysis of red blood cell membrane fluidity
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Ermanno Cordelli, Giuseppe Maulucci, Marco De Spirito, Alessandro Rizzi, Dario Pitocco, Paolo Soda
      Background and objective: Investigation of membrane fluidity by metabolic functional imaging opens up a new and important area of translational research in type 1 diabetes mellitus, being a useful and sensitive biomarker for disease monitoring and treatment. We investigate here how data on membrane fluidity can be used for diabetes monitoring. Methods: We present a decision support system that distinguishes between healthy subjects, type 1 diabetes mellitus patients, and type 1 diabetes mellitus patients with complications. It leverages on dual channel data computed from the physical state of human red blood cells membranes by means of features based on first- and second-order statistical measures as well as on rotation invariant co-occurrence local binary patterns. The experiments were carried out on a dataset of more than 1000 images belonging to 27 subjects. Results: Our method shows a global accuracy of 100%, outperforming also the state-of-the-art approach based on the glycosylated hemoglobin. Conclusions: The proposed recognition approach permits to achieve promising results.

      PubDate: 2018-05-31T09:59:53Z
  • Model selection for clustering of pharmacokinetic responses
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Rui P. Guerra, Alexandra M. Carvalho, Paulo Mateus
      Background and Objective: Pharmacokinetics comprises the study of drug absorption, distribution, metabolism and excretion over time. Clinical pharmacokinetics, focusing on therapeutic management, offers important insights towards personalised medicine through the study of efficacy and toxicity of drug therapies. This study is hampered by subject’s high variability in drug blood concentration, when starting a therapy with the same drug dosage. Clustering of pharmacokinetics responses has been addressed recently as a way to stratify subjects and provide different drug doses for each stratum. This clustering method, however, is not able to automatically determine the correct number of clusters, using an user-defined parameter for collapsing clusters that are closer than a given heuristic threshold. We aim to use information-theoretical approaches to address parameter-free model selection. Methods: We propose two model selection criteria for clustering pharmacokinetics responses, founded on the Minimum Description Length and on the Normalised Maximum Likelihood. Results: Experimental results show the ability of model selection schemes to unveil the correct number of clusters underlying the mixture of pharmacokinetics responses. Conclusions: In this work we were able to devise two model selection criteria to determine the number of clusters in a mixture of pharmacokinetics curves, advancing over previous works. A cost-efficient parallel implementation in Java of the proposed method is publicly available for the community.

      PubDate: 2018-05-28T17:52:24Z
  • Generalized fused group lasso regularized multi-task feature learning for
           predicting cognitive outcomes in Alzheimers disease
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Peng Cao, Xiaoli Liu, Hezi Liu, Jinzhu Yang, Dazhe Zhao, Min Huang, Osmar Zaiane
      Objective Alzheimers disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from Magnetic Resonance Imaging (MRI) measures. Recently, the multi-task feature learning (MTFL) methods have been widely studied to predict cognitive outcomes and select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, the existing MTFL assumes the correlation among all the tasks is uniform, and the task relatedness is modeled by encouraging a common subset of features with neglecting the inherent structure of tasks and MRI features. Methods In this paper, we proposed a generalized fused group lasso (GFGL) regularization to model the underlying structures, involving (1) a graph structure within tasks and (2) a group structure among the image features. Then, we present a multi-task learning framework (called GFGL–MTFL), combining the ℓ2, 1-norm with the GFGL regularization, to model the flexible structures. Results Through empirical evaluation and comparison with different baseline methods and the state-of-the-art MTL methods on data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we illustrate that the proposed GFGL–MTFL method outperforms other methods in terms of both Mean Squared Error (nMSE) and weighted correlation coefficient (wR). Improvements are statistically significant for most scores (tasks). Conclusions The experimental results with real and synthetic data demonstrate that incorporating the two prior structures by the generalized fused group lasso norm into the multi task feature learning can improve the prediction performance over several state-of-the-art competing methods, and the estimated correlation of the cognitive functions and the identification of cognition relevant imaging markers are clinically and biologically meaningful.

      PubDate: 2018-05-28T17:52:24Z
  • Sparse deformation prediction using Markove Decision Processes (MDP) for
           Non-rigid registration of MR image
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Tianyu Fu, Qin Li, Jianjun Zhu, Danni Ai, Yong Huang, Hong Song, Yurong Jiang, Yongtian Wang, Jian Yang
      Background and Objective A framework of sparse deformation prediction using Markove Decision Processes is proposed for achieving a rapid and accurate registration by providing a suitable initial deformation. Methods In the proposed framework, the tree is built based on the training set for each patch from the template image. The template patch is considered as the root. The node is the patch group in which multiple similar patches are extracted around a key point on the training image. Given the linkages between patch groups in the tree, MDP is introduced to select the optimal path with highest registration accuracy from each training patch to the template patch. The deformation between them is estimated along the selected path by patch-wise registration which can be realized by a non-learning-based method. Given the patches on a testing image, their best matching patches are fast chosen from the training patches and the corresponding deformations constitute a sparse deformation. A dense deformation for the entire test image is subsequently interpolated and used as an initial deformation for further registration. Results With the non-learning-based registration as the baseline method, the proposed framework is evaluated using three datasets of inter-subject brain MR images with three learning-based methods. Experimental results of the non-learning-based method using the proposed framework reveal that the computation time is reduced by fivefold after using the proposed framework. And, with the same baseline method, the proposed framework demonstrates the higher accuracy than three learning-based methods which predicts the initial deformation at image scale. The mean Dice of three datasets for the tissues of the brain are 73.52%, 70.73% and 64.82%, respectively. Conclusions The proposed framework rapidly registers the inter-subject brains and achieves the high mean Dice for the tissues of the brain.

      PubDate: 2018-05-28T17:52:24Z
  • MobilityAnalyser: A novel approach for automatic quantification of cell
           mobility on periodic patterned substrates using brightfield microscopy
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Ângela Carvalho, Tiago Esteves, Pedro Quelhas, Fernando Jorge Monteiro
      Background and objective Surface topography of biomaterials has been shown to have an effect on cells behaviour. Cell-material interactions can be visually characterized by assessing both cell shape and spreading at initial time-points and, its migration patterns, as a response to the underlying topography. Whilst many have reported the study of cell migration and shape with fluorescence labelling, the focus on evaluating cells response to surface topography is to observe, under real-time conditions, interactions between cells and surfaces. In this manuscript we present a novel approach to automatically detect and remove periodic background patterns in brightfield microscopy images in order to perform automatic cell mobility analysis. Methods The developed software, MobilityAnalyser, performs automatic tracking of unmarked cells and allows the user to manually correct any incorrectly detected or tracked cell. Human Mesenchymal Stem Cells (hMSCs) trajectory, migration distance, velocity and persistence were evaluated over line and pillar micropatterned SiO2 films and on a flat SiO2 control substrate. Results The developed software proved to be effective in automatically removing background patterns of both line and pillar shapes and in performing cell detection and tracking. MobilityAnalyser accurately measured cell mobility in a fraction of the time required for manual analysis and eliminated user subjectivity. The results obtained with the software confirmed how different topographies affect cell trajectory, migration pathways and velocities, with a statistically significant increase for micropatterned surfaces, when compared with the flat control. The persistence parameter also proved the influence of both patterns on the directionality of cell movement. Conclusions MobilityAnalyser is an automatic tool that removes periodic background patterns, detects and tracks cells, and provides cell mobility parameters that characterize the response of cells to different surface topographies. The software is freely available at:'id=1Fbb321ogLD19SlRjceMETNUqDHgpeBPl.

      PubDate: 2018-05-28T17:52:24Z
  • Multivariate approach for estimating the local spectral F-test and its
           application to the EEG during photic stimulation
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Leonardo Bonato Felix, Paulo Fábio Figueiredo Rocha, Eduardo Mazoni Andrade Marçal Mendes, Antonio Mauricio Ferreira Leite Miranda de Sá
      Background and objective The local spectral F-test (SFT) corresponds to a statistical way of assessing whether the spectrum of a signal is flat in the vicinity of a specific frequency. The power of this univariate test (comparing one frequency component  against its neighbours using only one signal) depends on the signal-to-noise ratio, which is fixed in the case of electroencephalogram (EEG) analysis. However, this limitation could be overcome by considering more signals in the analysis. Thus, this work presents an alternative multivariate approach for estimating the local SFT. Methods Probabilities of detection and false alarm studies were performed for this new detector using Monte Carlo simulations and theoretically whenever possible. The application was illustrated in recorded EEG data collected during photic stimulation. Results The results showed that it is worth using more channels if available, since the probability of detecting a response tends to increase with increasing number of signals. In the application to the EEG during photic stimulation, the best results were obtained by using N > 2 signals (around 30% more accurate when compared with the univariate case. The false positive levels were maintained below 5%). Conclusion Consequently, it is conjectured that it is always better to apply the proposed method if more than one EEG signal with the same signal-to-noise ratio (SNR) is available. For the case where the SNRs are different, a guideline has been given to improve the detection.

      PubDate: 2018-05-28T17:52:24Z
  • Betti number ratios as quantitative indices for bone morphometry in three
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Takashi Teramoto, Takeshi Kamiya, Taira Sakurai, Fuminori Kanaya
      Background and objective: Computational homology is an emerging mathematical tool for characterizing shapes of data. In this work, we present a methodology using computational homology for obtaining quantitative measurements of the connectivity in bone morphometry. We introduce the Betti number ratios as novel morphological descriptor for the classification of bone fine structures in three dimensions. Methods A total of 51 Japanese white rabbits were used to investigate the connectivity of bone trabeculae after the administration of alendronate in a tendon graft model in rabbits. They were divided into a control group C and an experimental group A. Knee joints specimens were harvested for examination of their bone trabecular structure by micro-CT. Applying the computational homology software to the reconstructed 3D image data, we extract the morphological feature of a steric bone structure as the Betti numbers set (β 0, β 1, β 2). The zeroth Betti number β 0 indicates the number of the connected components corresponding to isolated bone fragments. The first and second Betti numbers, β 1 and β 2, indicate the numbers of open pores and closed pores of bone trabeculae, corresponding to a 2D empty space enclosed by a 1D curve and a 3D empty space enclosed by a 2D surface, respectively. Results We define the Betti number ratios β 1/β 0 and β 2/β 0 to better distinguish the two groups A and C in the scatter plots. Testing the discriminant function line for 29 data points of A (22 data points of C), the 17 points (resp. 18 points) are correctly classified into group A (resp. C). The accuracy rate is 35/51. The classification results in terms of the Betti number ratios are consistent with the histomorphometric measurements observed by medical doctors. Conclusions: This study is the first application of computational homology to bone morphometry in three dimensions. We show the mathematical basis of the Betti numbers index which are useful in a statistical description of the topological features of sponge-like structures. The potential benefits associated with our method include both improved quantification and reproducibility for the stereology.

      PubDate: 2018-05-28T17:52:24Z
  • BIARAM: A process for analyzing correlated brain regions using association
           rule mining
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Pankush Kalgotra, Ramesh Sharda
      Background and objective Because examining correlated (vs. individual) brain activity is a superior method for locating neural correlates of a stimulus, using a network approach for analyzing brain activity is gaining interest. In this study, we propose and illustrate the use of association rule mining (ARM) to analyze brain regions that are activated simultaneously. ARM is commonly used in marketing and other disciplines to help determine items that might be purchased together. We apply this technique toward identifying correlated brain regions that may respond simultaneously to specific stimuli. Our objective is to introduce ARM, describe a process for converting neural images into viable datasets (for analyses), and suggest how to apply this process for generating insights about the brain's responses to specific stimuli (e.g. technology-associated interruptions). Methods We analyze electroencephalogram (EEG) data collected from 46 participants; convert brain waves into images via a source localization algorithm known as sLORETA (i.e., standardized low-resolution brain electromagnetic tomography); reorganize these into a “transactional” dataset; and generate association rules through ARM. Results We compare the results with more conventional methods for analyzing neuroimaging data. We show that there is a stronger correlation between frontal lobe and sublobar/insula regions after interruptions. This result would not be obvious from independent analysis of each region. Conclusions The main contribution of this paper is introducing ARM as a method for analyzing multiple images. We suggest that the biomedical community may apply this commonly available data mining technique to develop further insights about correlated regions affected by specific stimuli.
      Graphical abstract image

      PubDate: 2018-05-28T17:52:24Z
  • Convolutional neural network-based PSO for lung nodule false positive
           reduction on CT images
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Giovanni Lucca França da Silva, Thales Levi Azevedo Valente, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva, Marcelo Gattass
      Background and objective Detection of lung nodules is critical in CAD systems; this is because of their similar contrast with other structures and low density, which result in the generation of numerous false positives (FPs). Therefore, this study proposes a methodology to reduce the FP number using a deep learning technique in conjunction with an evolutionary technique. Method The particle swarm optimization (PSO) algorithm was used to optimize the network hyperparameters in the convolutional neural network (CNN) in order to enhance the network performance and eliminate the requirement of manual search. Results The methodology was tested on computed tomography (CT) scans from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) with the highest accuracy of 97.62%, sensitivity of 92.20%, specificity of 98.64%, and area under the receiver operating characteristic (ROC) curve of 0.955. Conclusion The results demonstrate the high performance-potential of the PSO algorithm in the identification of optimal CNN hyperparameters for lung nodule candidate classification into nodules and non-nodules, increasing the sensitivity rates in the FP reduction step of CAD systems.

      PubDate: 2018-05-28T17:52:24Z
  • Non-invasive detection of coronary artery disease in high-risk patients
           based on the stenosis prediction of separate coronary arteries
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Roohallah Alizadehsani, Mohammad Javad Hosseini, Abbas Khosravi, Fahime Khozeimeh, Mohamad Roshanzamir, Nizal Sarrafzadegan, Saeid Nahavandi
      Background and objective Cardiovascular diseases are an extremely widespread sickness and account for 17 million deaths in the world per annum. Coronary artery disease (CAD) is one of such diseases with an annual mortality rate of about 7 million. Thus, early diagnosis of CAD is of vital importance. Angiography is currently the modality of choice for the detection of CAD. However, its complications and costs have prompted researchers to seek alternative methods via machine learning algorithms. Methods The present study proposes a novel machine learning algorithm. The proposed algorithm uses three classifiers for detection of the stenosis of three coronary arteries, i.e., left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA) to get higher accuracy for CAD diagnosis. Results This method was applied on the extension of Z-Alizadeh Sani dataset which contains demographic, examination, ECG, and laboratory and echo data of 500 patients. This method achieves an accuracy, sensitivity and specificity rates of 96.40%, 100% and 88.1%, respectively for the detection of CAD. To our knowledge, such high rates of accuracy and sensitivity have not been attained elsewhere before. Conclusion This new algorithm reliably distinguishes those with normal coronary arteries from those with CAD which may obviate the need for angiography in the normal group.

      PubDate: 2018-05-28T17:52:24Z
  • Computer-aided prediction model for axillary lymph node metastasis in
           breast cancer using tumor morphological and textural features on
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Woo Kyung Moon, I-Ling Chen, Ann Yi, Min Sun Bae, Sung Ui Shin, Ruey-Feng Chang
      Background and objectives Axillary lymph node (ALN) status is a key indicator in assessing and determining the treatment strategy for patients with newly diagnosed breast cancer. Previous studies suggest that sonographic features of a primary tumor have the potential to predict ALN status in the preoperative staging of breast cancer. In this study, a computer-aided prediction (CAP) model as well as the tumor features for ALN metastasis in breast cancers were developed using breast ultrasound (US) images. Methods A total of 249 malignant tumors were acquired from 247 female patients (ages 20–84 years; mean 55 ± 11 years) to test the differences between the non-metastatic (130) and metastatic (119) groups based on various features. After applying semi-automatic tumor segmentation, 69 quantitative features were extracted. The features included morphology and texture of tumors inside a ROI of breast US image. By the backward feature selection and linear logistic regression, the prediction model was constructed and established to estimate the likelihood of ALN metastasis for each sample collected. Results In the experiments, the texture features showed higher performance for predicting ALN metastasis compared to morphology (Az, 0.730 vs 0.667). The difference, however, was not statistically significant (p-values > 0.05). Combining the textural and morphological features, the accuracy, sensitivity, specificity, and Az value achieved 75.1% (187/249), 79.0% (94/119), 71.5% (93/130), and 0.757, respectively. Conclusions The proposed CAP model, which combines textural and morphological features of primary tumor, may be a useful method to determine the ALN status in patients with breast cancer.

      PubDate: 2018-05-28T17:52:24Z
  • A mobile computer aided system for optic nerve head detection
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Yaroub Elloumi, Mohamed Akil, Nasser Kehtarnavaz
      Background and objective The detection of optic nerve head (ONH) in retinal fundus images plays a key role in identifying Diabetic Retinopathy (DR) as well as other abnormal conditions in eye examinations. This paper presents a method and its associated software towards the development of an Android smartphone app based on a previously developed ONH detection algorithm. The development of this app and the use of the d-Eye lens which can be snapped onto a smartphone provide a mobile and cost-effective computer-aided diagnosis (CAD) system in ophthalmology. In particular, this CAD system would allow eye examination to be conducted in remote locations with limited access to clinical facilities. Methods A pre-processing step is first carried out to enable the ONH detection on the smartphone platform. Then, the optimization steps taken to run the algorithm in a computationally and memory efficient manner on the smartphone platform is discussed. Results The smartphone code of the ONH detection algorithm was applied to the STARE and DRIVE databases resulting in about 96% and 100% detection rates, respectively, with an average execution time of about 2 s and 1.3 s. In addition, two other databases captured by the d-Eye and iExaminer snap-on lenses for smartphones were considered resulting in about 93% and 91% detection rates, respectively, with an average execution time of about 2.7 s and 2.2 s, respectively.

      PubDate: 2018-05-28T17:52:24Z
  • Analysis of trunk rolling in Parkinson's disease patients using a mattress
           mobility detection system
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Shang-Lin Chiang, Chueh-Ho Lin, Yaw-Don Hsu, Shun-Hwa Wei, Wen-Hsu Sung, Liang-Hsuan Lu, Shin-Tsu Chang, Tsung-Yen Ho, Yu-Ping Shen, Liang-Cheng Chen, Chia-Huei Lin
      Background and Objective Parkinson's disease (PD) is a neurodegenerative condition characterized by motor dysfunction and various types of non-motor impairments. The reaction time and movement time are reported to become more severe delayed in worse PD patients. Most tools for evaluating motor impairment are limited by relying on subjective observations and being qualitative in design. The aim of this study was to investigate trunk rolling performance in PD patients by using a recently developed system to detect turning in bed. Methods The study included 20 PD patients and 42 healthy controls. A mattress mobility detection system was employed for quantitative measurements. Each test session consisted of subjects starting by lying in a supine position on a bed and rolling 10 times onto their left side and 10 times onto their right side. Strain gauges mounted under the feet of the bed recorded changes in the center of pressure (CoP). Results For turning back, the patients compared with the controls had significantly longer movement time (P = 0.017), longer time to peak counteraction (P = 0.001), larger ratio of peak counteraction to movement time (P = 0.006), shorter CoP displacement (P < 0.0001), slower turning speed (P = 0.000), weaker peak counteraction (P = 0.013), and smaller ratio of peak counteraction to weight (P = 0.032). Results for turning over were similar except there was no significant difference in the ratio of peak counteraction to weight. Conclusions The mattress mobility detection system was useful for objectively assessing trunk rolling performance of PD patients. Improved assessment of trunk function in PD patients could lead to better treatments and improved rehabilitation procedures.

      PubDate: 2018-05-28T17:52:24Z
  • Image quilting and wavelet fusion for creation of synthetic microscopy
           nuclei images
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Dimitris Glotsos, Spiros Kostopoulos, Panagiota Ravazoula, Dionisis Cavouras
      Background and Objective In this study a texture simulation methodology is proposed for composing synthetic tissue microscopy images that could serve as a quantitative gold standard for the evaluation of the reliability, accuracy and performance of segmentation algorithms in computer-aided diagnosis. Methods A library of background and nuclei regions was generated using pre-segmented Haematoxylin and Eosin images of brain tumours. Background image samples were used as input to an image quilting algorithm that produced the synthetic background image. Randomly selected pre-segmented nuclei were randomly fused on the synthetic background using a wavelet-based fusion approach. To investigate whether the produced synthetic images are meaningful and similar to real world images, two different tests were performed, one qualitative by an experienced histopathologist and one quantitative using the normalized mutual information and the Kullback-Leibler tests. To illustrate the challenges that synthetic images may pose to object recognition algorithms, two segmentation methodologies were utilized for nuclei detection, one based on the Otsu thresholding and another based on the seeded region growing approach. Results Results showed a satisfactory to good resemblance of the synthetic with the real world images according to both qualitative and quantitative tests. The segmentation accuracy was slightly higher for the seeded region growing algorithm (87.2%) than the Otsu's algorithm (86.3%). Conclusions Since we know the exact coordinates of the regions of interest within the synthesised images, these images could then serve as a ‘gold standard’ for evaluation of segmentation algorithms in computer-aided diagnosis in tissue microscopy.

      PubDate: 2018-05-28T17:52:24Z
  • A Quasi-probabilistic distribution model for EEG Signal classification by
           using 2-D signal representation
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Cagatay Murat Yilmaz, Cemal Kose, Bahar Hatipoglu
      Background and Objective: Electroencephalography (EEG) is a method that measures and records the electrical activity of the human brain. These biomedical signals are currently being actively used in many research fields and have a wide range of potential uses in brain–computer interfaces (BCIs). The main aim of the present work is to improve the classification of EEG patterns for EEG-based BCI systems. Methods: In this paper, we presented a classification approach for EEG-based BCIs. For this purpose, in the training stage, 2-D representations of signals were extracted and a quasi-probabilistic learning model was built for binary classification. In the testing stage, the estimation of class membership probability was performed with an untrained sub-data set. To confirm the validity of the proposed method, we conducted experiments on the BCI Competition 2003 Data Sets (Ia and Ib). The classification performances were evaluated for accuracy, sensitivity, specificity and F-measure measurements using the five-fold leave-one-out cross-validation technique ten times. Results: The proposed method yielded an average classification accuracy of 95.54% (with sensitivity and specificity of 100.00% and 91.80% respectively) for Data Set Ia and accuracy of 72.37% (with sensitivity and specificity of 75.76% and 69.77% respectively) for Data Set Ib, which are the highest rates ever reported for both data sets. Conclusions: It is apparent from the results that the proposed method has potential and can assist in the development of effective EEG-based BCIs. The advantage of this method lies in its relatively simple algorithm and easy computational implementation. The experimental results also showed that the selection of relevant channels is an important step in developing efficient EEG-based BCI systems.

      PubDate: 2018-05-28T17:52:24Z
  • Improved lung nodule diagnosis accuracy using lung CT images with
           uncertain class
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Zhiqiong Wang, Junchang Xin, Peishun Sun, Zhixiang Lin, Yudong Yao, Xiaosong Gao
      Background and objective: Among all malignant tumors, lung cancer ranks in the top in mortality rate. Pulmonary nodule is the early manifestation of lung cancer, and plays an important role in its discovery, diagnosis and treatment. The technology of medical imaging has encountered a rapid development in recent years, thus the amount of pulmonary nodules can be discovered are on the raise, which means even tiny or minor changes in lung can be recorded by the CT images. This paper proposes a pulmonary nodule computer aided diagnosis (CAD) based on semi-supervised extreme learning machine(SS-ELM). Methods: First, the feature model based on the pulmonary nodules regions of lung CT images is established. After that, the same feature data sets have been put into ELM, support vector machine (SVM) methods, probabilistic neural network (PNN) and multilayer perceptron (MLP) so as to compare the performance of the methods. ELM turned out to have better performance in training time and testing accuracy compared with SVM, PNN and MLP. Then, we propose a pulmonary nodules computer aided diagnosis algorithm based on semi-supervised ELM (SS-ELM), which enables both certain class feature sets with labels and unlabeled feature sets to be input for training and computer aided diagnosing. This algorithm has provided a solution for the using of uncertain class data and improve the testing accuracy of benign and malignant diagnosis. Results: 1018 sets of thoracic CT images from the Lung Database Consortium and Image Database Resource Initiative (LIDC-IDRI) have been used in experiment in order to test the effectiveness of the algorithm. Compared with ELM, the pulmonary nodules CAD based on SS-ELM has better testing accuracy performance. Conclusions: We have proposed a pulmonary nodule CAD system based on SS-ELM, which achieving better generalization performance at faster learning speed and higher testing accuracy than ELM, SVM, PNN and MLP. The SS-ELM based pulmonary nodules CAD has been proposed to solve the problem of uncertain class data using.

      PubDate: 2018-05-28T17:52:24Z
  • Stand-alone lumbar cage subsidence: A biomechanical sensitivity study of
           cage design and placement.
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Andrea Calvo-Echenique, José Cegoñino, Raúl Chueca, Amaya Pérez-del Palomar
      Background and objective Spinal degeneration and instability are commonly treated with interbody fusion cages either alone or supplemented with posterior instrumentation with the aim to immobilise the segment and restore intervertebral height. The purpose of this work is to establish a tool which may help to understand the effects of intervertebral cage design and placement on the biomechanical response of a patient-specific model to help reducing post-surgical complications such as subsidence and segment instability. Methods A 3D lumbar functional spinal unit (FSU) finite element model was created and a parametric model of an interbody cage was designed and introduced in the FSU. A Drucker–Prager Cap plasticity formulation was used to predict plastic strains and bone failure in the vertebrae. The effect of varying cage size, cross-sectional area, apparent stiffness and positioning was evaluated under 500 N preload followed by 7.5 Nm multidirectional rotation and the results were compared with the intact model. Results The most influential cage parameters on the FSU were size, curvature congruence with the endplates and cage placement. Segmental stiffness was higher when increasing the cross-sectional cage area in all loading directions and when the cage was anteriorly placed in all directions but extension. In general, the facet joint forces were reduced by increasing segmental stiffness. However, these forces were higher than in the intact model in most of the cases due to the displacement of the instantaneous centre of rotation. The highest plastic deformations took place at the caudal vertebra under flexion and increased for cages with greater stiffness. Thus, wider cages and a more anteriorly placement would increase the volume of failed bone and, therefore, the risk of subsidence. Conclusions Cage geometry plays a crucial role in the success of lumbar surgery. General considerations such as larger cages may be applied as a guideline, but parameters such as curvature or cage placement should be determined for each specific patient. This model provides a proof-of-concept of a tool for the preoperative evaluation of lumbar surgical outcomes.

      PubDate: 2018-05-28T17:52:24Z
  • Fine-grained leukocyte classification with deep residual learning for
           microscopic images
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Feiwei Qin, Nannan Gao, Yong Peng, Zizhao Wu, Shuying Shen, Artur Grudtsin
      Background and objective: Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past development of machine learning techniques, for example, extracting distinctive features from raw microscopic images are difficult, the widely used SVM classifier only has relative few parameters to tune, these methods cannot efficiently handle fine-grained classification cases when the white blood cells have up to 40 categories. Methods: Based on deep learning theory, a systematic study is conducted on finer leukocyte classification in this paper. A deep residual neural network based leukocyte classifier is constructed at first, which can imitate the domain expert’s cell recognition process, and extract salient features robustly and automatically. Then the deep neural network classifier’s topology is adjusted according to the prior knowledge of white blood cell test. After that the microscopic image dataset with almost one hundred thousand labeled leukocytes belonging to 40 categories is built, and combined training strategies are adopted to make the designed classifier has good generalization ability. Results: The proposed deep residual neural network based classifier was tested on microscopic image dataset with 40 leukocyte categories. It achieves top-1 accuracy of 77.80%, top-5 accuracy of 98.75% during the training procedure. The average accuracy on the test set is nearly 76.84%. Conclusions: This paper presents a fine-grained leukocyte classification method for microscopic images, based on deep residual learning theory and medical domain knowledge. Experimental results validate the feasibility and effectiveness of our approach. Extended experiments support that the fine-grained leukocyte classifier could be used in real medical applications, assist doctors in diagnosing diseases, reduce human power significantly.

      PubDate: 2018-05-28T17:52:24Z
  • Computerized decision support for beneficial home-based exercise
           rehabilitation in patients with cardiovascular disease
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Andreas Triantafyllidis, Dimitris Filos, Roselien Buys, Jomme Claes, Véronique Cornelissen, Evangelia Kouidi, Anargyros Chatzitofis, Dimitris Zarpalas, Petros Daras, Deirdre Walsh, Catherine Woods, Kieran Moran, Nicos Maglaveras, Ioanna Chouvarda
      Background Exercise-based rehabilitation plays a key role in improving the health and quality of life of patients with Cardiovascular Disease (CVD). Home-based computer-assisted rehabilitation programs have the potential to facilitate and support physical activity interventions and improve health outcomes. Objectives We present the development and evaluation of a computerized Decision Support System (DSS) for unsupervised exercise rehabilitation at home, aiming to show the feasibility and potential of such systems toward maximizing the benefits of rehabilitation programs. Methods The development of the DSS was based on rules encapsulating the logic according to which an exercise program can be executed beneficially according to international guidelines and expert knowledge. The DSS considered data from a prescribed exercise program, heart rate from a wristband device, and motion accuracy from a depth camera, and subsequently generated personalized, performance-driven adaptations to the exercise program. Communication interfaces in the form of RESTful web service operations were developed enabling interoperation with other computer systems. Results The DSS was deployed in a computer-assisted platform for exercise-based cardiac rehabilitation at home, and it was evaluated in simulation and real-world studies with CVD patients. The simulation study based on data provided from 10 CVD patients performing 45 exercise sessions in total, showed that patients can be trained within or above their beneficial HR zones for 67.1 ± 22.1% of the exercise duration in the main phase, when they are guided with the DSS. The real-world study with 3 CVD patients performing 43 exercise sessions through the computer-assisted platform, showed that patients can be trained within or above their beneficial heart rate zones for 87.9 ± 8.0% of the exercise duration in the main phase, with DSS guidance. Conclusions Computerized decision support systems can guide patients to the beneficial execution of their exercise-based rehabilitation program, and they are feasible.

      PubDate: 2018-05-28T17:52:24Z
  • Multiuser communication scheme based on binary phase-shift keying and
           chaos for telemedicine
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): J.A. Michel-Macarty, M.A. Murillo-Escobar, R.M. López-Gutiérrez, C. Cruz-Hernández, L. Cardoza-Avendaño
      Background and objectives Currently, telemedicine is levered upon the improvement in communication network technology such as Body Area Sensor Networks (BASN) to provided biomedicine solutions. Nevertheless, information security is an important issue since biomedical data is exchanged through insecure channels, which exposes private information that can be intercepted by malicious intruder. Therefore, secure communication protocols for multiuser networks in telemedicine applications are a big challenge. Recent chaos-based encryption works have been conducted in the area of medical secure communications with high security capabilities. However, none of them has considered multiuser network, which is used in several e-health applications. Up to our knowledge, the proposed protocol is the first attempt to consider this service in secure telemedicine. In this paper, we propose a novel scheme based on binary phase-shift key (BPSK) and chaos to provide information security at biosignals in a multiuser network system transmitting data over single channel. Methods The proposed scheme uses the two-dimensional Hénon map with enhance pseudorandom sequences and CDMA technique to achieve multiuser encryption process and transmit data over a single channel. We use biosignals such as electrocardiograms (ECG) and blood pressure (PB) signals from PhisioBank ATM data base for simulation results at MatLab software. We evaluate the security and performance by determining the secret key space, secret key sensitivity, resistance against noise attack with quality analysis by using BER, MSE, and PSNR, encryption-decryption time, and throughput. Results In simulations tests, biosignals of ECG and BP in a BANS network are encrypted and transmitted over shared wireless channels and just authorized medical personal can retrieve such information with corresponding secret key from the cryptogram, that appears as noise to any intruder. The proposed multiuser scheme support high noise and interference attacks efficiently in contrast with classic chaos-based encryption works for telemedicine, where some scenarios are simulated with very low BER, very low MSE, and high PSNR between plain biosignals and recovered biosignals when high AWGN noise is added to encrypted-transmitted signal. In addition, the encryption process presents enough key space and high sensitivity at secret key. A comparative analysis of proposed method and recent existing works was also presented. Conclusions Patients can be monitored and diagnosed opportunely remotely and all their medical information is transmitted securely to the correct specialist. Also, it is possible to transmit several electrophysiological signals in a single channel in a secure multiuser network at low cost optimizing the use of available bandwidth for telemedicine applications.

      PubDate: 2018-05-28T17:52:24Z
  • How the gender of a victim character in a virtual scenario created to
           learn CPR protocol affects student nurses’ performance
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Imma Boada, Antonio Rodriguez-Benitez, Santiago Thió-Henestrosa, Josep Olivet, Josep Soler
      Background and objective Virtual simulations recreate scenarios where student nurses can practice procedures in a safe and supervised manner and with no risk to the patient. Virtual scenarios include digital characters that reproduce human actions. Generally, these characters are modeled as males and restricted roles are assigned to females. Our objective is to evaluate how the character gender of a victim in a scenario created to practice the cardiopulmonary resuscitation protocol (CPR) affects performance of student nurses. Methods Three virtual scenarios with cardiac arrest victims modeled as males or females were assigned to 41 students of the Nursing Faculty to practice the CPR protocol. We evaluated student performance with respect to the time to remove clothes, the time to perform the CPR maneuver, and the hands position for CPR. Chi-square, Fisher exact, and Mann–Whitney U were used to test primary outcome measures in the experimental design of victim character sex (male vs. female) and student sex (men vs. women). Results The analysis performed did not find statistically differences in time to remove clothes or in time to start CPR. With respect to hands placement we also did not find significant difference in any of the cases. Conclusion Nurse student actions are not influenced by the character gender of the victim. Excellent results with respect to hands placement to start CPR are obtained. Virtual scenarios can be a suitable strategy to reduce gender differences in gender sensitive situations such as CPR performance.

      PubDate: 2018-05-28T17:52:24Z
  • Assessment of thermal necrosis risk regions for different bone qualities
           as a function of drilling parameters
    • Abstract: Publication date: August 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 162
      Author(s): Yung-Chuan Chen, Yuan-Kun Tu, Yi-Jung Tsai, Yi-Shan Tsai, Cheng-Yo Yen, Shih-Chieh Yang, Chih-Kun Hsiao
      Background and objective During bone drilling, the heat generated by friction depends directly on bone quality and surgical parameters. Excessive bone temperatures may cause thermal necrosis around the pilot hole, weaken the purchase of inserted screws, and in turn reduce the stability of screw fixation. A few studies have addressed the key parameters of drilling, such as the rotation speed of the drill-bit, feed force (axial force), feed rate, tool type, and tip geometry of drill-bits. Nevertheless, in the literature, information on the relationship between bone quality and thermally affected regions is still lacking. This study employed a three-dimensional dynamic elastoplastic finite element model to evaluate the influence of surgical parameters on the bone temperature elevation and assess the risk region of thermal necrosis for different bone qualities as a function of drilling parameters. Methods To ascertain the heat generation rate and the high-risk region of thermal necrosis, the effects of bone quality, feed rate, feed force, and drill-bit diameter on the bone temperature elevation were explained using a three-dimensional dynamic elastoplastic finite element model, which was validated through experimental measurements. Results The bone temperature was affected by the drilling parameters; the maximum temperature was attained at the junction of cancellous and cortical bones. The bone temperature increased with cortical bone thickness, bone density, and drill-bit diameter, and it decreased with the drilling speed and feed force. Conclusions The present model could assess the risk region of thermal necrosis by accurately analyzing the bone temperature elevation for various bone qualities, feed forces, and feed rates. The bone temperature increased with the bone mineral density and cortical bone thickness. The highest bone temperature and maximum necrosis region were found near the junction of cortical and cancellous bones. Increasing the drilling speed or feed force can minimize the bone temperature elevation and the risk range of thermal necrosis.

      PubDate: 2018-05-28T17:52:24Z
  • Potentiality of deep learning application in healthcare
    • Abstract: Publication date: July 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 161
      Author(s): Hsuan-Chia Yang, Md. Mohaimenul Islam, Yu-Chuan (Jack) Li

      PubDate: 2018-05-28T17:52:24Z
  • Spiral waves characterization: Implications for an automated cardiodynamic
           tissue characterization
    • Abstract: Publication date: July 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 161
      Author(s): Celal Alagoz, Andrew R. Cohen, Daniel R. Frisch, Birkan Tunç, Saran Phatharodom, Allon Guez
      Background and objective: Spiral waves are phenomena observed in cardiac tissue especially during fibrillatory activities. Spiral waves are revealed through in-vivo and in-vitro studies using high density mapping that requires special experimental setup. Also, in-silico spiral wave analysis and classification is performed using membrane potentials from entire tissue. In this study, we report a characterization approach that identifies spiral wave behaviors using intracardiac electrogram (EGM) readings obtained with commonly used multipolar diagnostic catheters that perform localized but high-resolution readings. Specifically, the algorithm is designed to distinguish between stationary, meandering, and break-up rotors. Methods: The clustering and classification algorithms are tested on simulated data produced using a phenomenological 2D model of cardiac propagation. For EGM measurements, unipolar-bipolar EGM readings from various locations on tissue using two catheter types are modeled. The distance measure between spiral behaviors are assessed using normalized compression distance (NCD), an information theoretical distance. NCD is a universal metric in the sense it is solely based on compressibility of dataset and not requiring feature extraction. We also introduce normalized FFT distance (NFFTD) where compressibility is replaced with a FFT parameter. Results: Overall, outstanding clustering performance was achieved across varying EGM reading configurations. We found that effectiveness in distinguishing was superior in case of NCD than NFFTD. We demonstrated that distinct spiral activity identification on a behaviorally heterogeneous tissue is also possible. Conclusions: This report demonstrates a theoretical validation of clustering and classification approaches that provide an automated mapping from EGM signals to assessment of spiral wave behaviors and hence offers a potential mapping and analysis framework for cardiac tissue wavefront propagation patterns.

      PubDate: 2018-05-28T17:52:24Z
  • Integrating genomic data and pathological images to effectively predict
           breast cancer clinical outcome
    • Abstract: Publication date: July 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 161
      Author(s): Dongdong Sun, Ao Li, Bo Tang, Minghui Wang
      Background and objective Breast cancer is a leading cause of death from cancer for females. The high mortality rate of breast cancer is largely due to the complexity among invasive breast cancer and its significantly varied clinical outcomes. Therefore, improving the accuracy of breast cancer survival prediction has important significance and becomes one of the major research areas. Nowadays many computational models have been proposed for breast cancer survival prediction, however, most of them generate the predictive models by employing only the genomic data information and few of them consider the complementary information from pathological images. Methods In our study, we introduce a novel method called GPMKL based on multiple kernel learning (MKL), which efficiently employs heterogeneous information containing genomic data (gene expression, copy number alteration, gene methylation, protein expression) and pathological images. With above heterogeneous features, GPMKL is proposed to execute feature fusion which is embedded in breast cancer classification. Results Performance analysis of the GPMKL model indicates that the pathological image information plays a critical part in accurately predicting the survival time of breast cancer patients. Furthermore, the proposed method is compared with other existing breast cancer survival prediction methods, and the results demonstrate that the proposed framework with pathological images performs remarkably better than the existing survival prediction methods. Conclusions All results performed in our study suggest that the usefulness and superiority of GPMKL in predicting human breast cancer survival.

      PubDate: 2018-05-28T17:52:24Z
  • A vessel segmentation method for serialized cerebralvascular DSA images
           based on spatial feature point set of rotating coordinate system
    • Abstract: Publication date: July 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 161
      Author(s): Bin Liu, Qianfeng Jiang, Wenpeng Liu, Mingzhe Wang, Song Zhang, Xiaohui Zhang, Bingbing Zhang, Zongge Yue
      Cerebrovascular pathology is one of the main fatal diseases which seriously affect the human's health. Extracting the accurate image of cerebral vascular tissue is the key of clinical diagnosis. However, the motion artifacts in DSA images seriously affected the quality of vascular subtraction image. In this paper, an automatic and accurate segmentation method is presented to extract the vascular region in the live image of brain. Firstly, a coarse registration for the live image and the mask image is implemented. And then, the SIFT algorithm is utilized to detect geometrical feature points in the serialized subtraction images. After that, a spatial model of rotating coordinate system and a calculative strategy of contextual information are designed to eliminate the error feature points. Finally, based on a dynamic threshold method, the blood vessel image can be obtained by region growing. The context information in the adjacent subtraction images is fully used. The experimental result shows that the segmented cerebral vascular image is satisfactory. This method can provide accurate vessel image data for the clinical operation based on DSA interventional therapy.
      Graphical abstract image

      PubDate: 2018-05-28T17:52:24Z
  • Automated segmentation of the atrial region and fossa ovalis towards
           computer-aided planning of inter-atrial wall interventions
    • Abstract: Publication date: July 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 161
      Author(s): Pedro Morais, João L. Vilaça, Sandro Queirós, Alberto Marchi, Felix Bourier, Isabel Deisenhofer, Jan D'hooge, João Manuel R.S. Tavares
      Background and objective Image-fusion strategies have been applied to improve inter-atrial septal (IAS) wall minimally-invasive interventions. Hereto, several landmarks are initially identified on richly-detailed datasets throughout the planning stage and then combined with intra-operative images, enhancing the relevant structures and easing the procedure. Nevertheless, such planning is still performed manually, which is time-consuming and not necessarily reproducible, hampering its regular application. In this article, we present a novel automatic strategy to segment the atrial region (left/right atrium and aortic tract) and the fossa ovalis (FO). Methods The method starts by initializing multiple 3D contours based on an atlas-based approach with global transforms only and refining them to the desired anatomy using a competitive segmentation strategy. The obtained contours are then applied to estimate the FO by evaluating both IAS wall thickness and the expected FO spatial location. Results The proposed method was evaluated in 41 computed tomography datasets, by comparing the atrial region segmentation and FO estimation results against manually delineated contours. The automatic segmentation method presented a performance similar to the state-of-the-art techniques and a high feasibility, failing only in the segmentation of one aortic tract and of one right atrium. The FO estimation method presented an acceptable result in all the patients with a performance comparable to the inter-observer variability. Moreover, it was faster and fully user-interaction free. Conclusions Hence, the proposed method proved to be feasible to automatically segment the anatomical models for the planning of IAS wall interventions, making it exceptionally attractive for use in the clinical practice.
      Graphical abstract image

      PubDate: 2018-05-28T17:52:24Z
  • A computerized method for evaluating scoliotic deformities using
           elliptical pattern recognition in X-ray spine images
    • Abstract: Publication date: July 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 161
      Author(s): Alan Petrônio Pinheiro, Júlio Cézar Coelho, Antônio C. Paschoarelli Veiga, Tomaž Vrtovec
      Background and Objective Several studies have evaluated the reproducibility of the Cobb angle for measuring the degree of scoliotic deformities from X-ray spine images, and proposed different geometric models for describing the spinal curvature. The ellipse was shown to be an adequate geometric form, but was not yet applied for the identification and quantification of scoliotic curvatures. The purpose of this study is therefore to propose and validate a novel computerized methodology for the detection of elliptical patterns from X-ray images to evaluate the extent of the underlying scoliotic deformity. Methods For anteroposterior each X-ray spine image, the spine curve is first reconstructed from vertebral centroids. The ellipse that best fits to the obtained spine curve is the found within a least square and genetic algorithm optimization framework. The geometric parameters of the resulting best fit ellipse are finally used to define an index that quantifies the spinal curvature. Results The proposed methodology was validated on three synthetic images and then successfully applied to 20 clinical anteroposterior X-ray spine images of patients with a different degree of scoliotic deformity, with the resulting maximal relative error of 3% for the synthetic images and an overall error of 0.5 ± 0.4 mm (mean ± standard deviation) for the clinical cases. Conclusions The results indicate that the proposed computerized methodology is able to reliably reproduce scoliotic curvatures using the geometric parameters of the underlying ellipses. In comparison to conventional approaches, the proposed methodology potentially produces less errors, requires a relatively low observer interaction, takes into account all vertebrae within the observed scoliotic deformity, and allows for both qualitative and quantitative evaluations that may complement the diagnosis, study and treatment of scoliosis.

      PubDate: 2018-05-28T17:52:24Z
  • Development of an adaptive pulmonary simulator for in vitro analysis of
           patient populations and patient-specific data
    • Abstract: Publication date: July 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 161
      Author(s): Jacob M. King, Clint A. Bergeron, Charles E. Taylor
      Background and objective Patient-specific modeling (PSM) is gaining more attention from researchers due to its ability to potentially improve diagnostic capabilities, guide the design of intervention procedures, and optimize clinical management by predicting the outcome of a particular treatment and/or surgical intervention. Due to the hemodynamic diversity of specific patients, an adaptive pulmonary simulator (PS) would be essential for analyzing the possible impact of external factors on the safety, performance, and reliability of a cardiac assist device within a mock circulatory system (MCS). In order to accurately and precisely replicate the conditions within the pulmonary system, a PS should not only account for the ability of the pulmonary system to supply blood flow at specific pressures, but similarly consider systemic outflow dynamics. This would provide an accurate pressure and flow rate return supply back into the left ventricular section of the MCS (i.e. the initial conditions of the left heart). Methods Employing an embedded Windkessel model, a control system model was developed utilizing MathWorks’ Simulink® Simscape™. Following a verification and validation (V&V) analysis approach, a PI-controlled closed-loop hydraulic system was developed using Simscape™. This physical system modeling tool was used to (1) develop and control the in silico system during verification studies and (2) simulate pulmonary performance for validation of this control architecture. Results The pulmonary Windkessel model developed is capable of generating the left atrial pressure (LAP) waveform from given pulmonary factors, aortic conditions, and systemic variables. Verification of the adaptive PS's performance and validation of this control architecture support this modeling methodology as an effective means of reproducing pulmonary pressure waveforms and systemic outflow conditions, unique to a particular patient. Adult and geriatric with and without Heart Failure and a Normal Ejection Fraction (HFNEF) are presented. Conclusions The adaptability of this modelling approach allows for the simulation of pulmonary conditions without the limitations of a dedicated hardware platform for use in in vitro investigations.

      PubDate: 2018-05-28T17:52:24Z
  • Automated EEG-based screening of depression using deep convolutional
           neural network
    • Abstract: Publication date: July 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 161
      Author(s): U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Hojjat Adeli, D. P Subha
      In recent years, advanced neurocomputing and machine learning techniques have been used for Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a novel computer model is presented for EEG-based screening of depression using a deep neural network machine learning approach, known as Convolutional Neural Network (CNN). The proposed technique does not require a semi-manually-selected set of features to be fed into a classifier for classification. It learns automatically and adaptively from the input EEG signals to differentiate EEGs obtained from depressive and normal subjects. The model was tested using EEGs obtained from 15 normal and 15 depressed patients. The algorithm attained accuracies of 93.5% and 96.0% using EEG signals from the left and right hemisphere, respectively. It was discovered in this research that the EEG signals from the right hemisphere are more distinctive in depression than those from the left hemisphere. This discovery is consistent with recent research and revelation that the depression is associated with a hyperactive right hemisphere. An exciting extension of this research would be diagnosis of different stages and severity of depression and development of a Depression Severity Index (DSI).
      Graphical abstract image

      PubDate: 2018-05-28T17:52:24Z
  • Shearlet and contourlet transforms for analysis of electrocardiogram
    • Abstract: Publication date: July 2018
      Source:Computer Methods and Programs in Biomedicine, Volume 161
      Author(s): Paulo Amorim, Thiago Moraes, Dalton Fazanaro, Jorge Silva, Helio Pedrini
      Background and Objective: Cardiac arrhythmia is an abnormal variation in the heart electrical activity that affects millions of people worldwide. Electrocardiogram (ECG) signals have been widely used to assess and diagnose cardiac abnormalities. Methods: A novel methodology based on shearlet and contourlet transforms for automatically classify an input ECG signal into different heart beat types is proposed and evaluated in this work. Classifiers are trained through a set of features extracted from these time-frequency coefficients. Results: Tests are conducted on MIT-BIH data set to demonstrate the effectiveness of the proposed classification method. The shearlet and contourlet transforms achieved high classification accuracy rates. Conclusions: The developed system can help cardiologists obtain structural and functional information of the heart by means of ECG patterns, improving their diagnostic tasks.

      PubDate: 2018-05-28T17:52:24Z
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