Publisher: Hindawi   (Total: 343 journals)

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Showing 1 - 200 of 343 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.343, CiteScore: 1)
Active and Passive Electronic Components     Open Access   (Followers: 8, SJR: 0.136, CiteScore: 0)
Advances in Acoustics and Vibration     Open Access   (Followers: 51, SJR: 0.147, CiteScore: 0)
Advances in Aerospace Engineering     Open Access   (Followers: 66)
Advances in Agriculture     Open Access   (Followers: 12)
Advances in Artificial Intelligence     Open Access   (Followers: 22)
Advances in Astronomy     Open Access   (Followers: 51, SJR: 0.257, CiteScore: 1)
Advances in Bioinformatics     Open Access   (Followers: 20, SJR: 0.565, CiteScore: 2)
Advances in Biology     Open Access   (Followers: 11)
Advances in Chemistry     Open Access   (Followers: 35)
Advances in Civil Engineering     Open Access   (Followers: 51, SJR: 0.539, CiteScore: 1)
Advances in Computer Engineering     Open Access   (Followers: 8)
Advances in Condensed Matter Physics     Open Access   (Followers: 11, SJR: 0.315, CiteScore: 1)
Advances in Decision Sciences     Open Access   (Followers: 4, SJR: 0.303, CiteScore: 1)
Advances in Electrical Engineering     Open Access   (Followers: 52)
Advances in Electronics     Open Access   (Followers: 101)
Advances in Emergency Medicine     Open Access   (Followers: 16)
Advances in Endocrinology     Open Access   (Followers: 6)
Advances in Environmental Chemistry     Open Access   (Followers: 10)
Advances in Epidemiology     Open Access   (Followers: 9)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.161, CiteScore: 1)
Advances in Geology     Open Access   (Followers: 19)
Advances in Geriatrics     Open Access   (Followers: 6)
Advances in Hematology     Open Access   (Followers: 13, SJR: 0.661, CiteScore: 2)
Advances in Hepatology     Open Access   (Followers: 3)
Advances in High Energy Physics     Open Access   (Followers: 26, SJR: 0.866, CiteScore: 2)
Advances in Human-Computer Interaction     Open Access   (Followers: 21, SJR: 0.186, CiteScore: 1)
Advances in Materials Science and Engineering     Open Access   (Followers: 31, SJR: 0.315, CiteScore: 1)
Advances in Mathematical Physics     Open Access   (Followers: 9, SJR: 0.218, CiteScore: 1)
Advances in Medicine     Open Access   (Followers: 3)
Advances in Meteorology     Open Access   (Followers: 24, SJR: 0.48, CiteScore: 1)
Advances in Multimedia     Open Access   (Followers: 1, SJR: 0.173, CiteScore: 1)
Advances in Nonlinear Optics     Open Access   (Followers: 7)
Advances in Numerical Analysis     Open Access   (Followers: 9)
Advances in Nursing     Open Access   (Followers: 37)
Advances in Operations Research     Open Access   (Followers: 13, SJR: 0.205, CiteScore: 1)
Advances in Optical Technologies     Open Access   (Followers: 4, SJR: 0.214, CiteScore: 1)
Advances in Optics     Open Access   (Followers: 9)
Advances in OptoElectronics     Open Access   (Followers: 6, SJR: 0.141, CiteScore: 0)
Advances in Orthopedics     Open Access   (Followers: 11, SJR: 0.922, CiteScore: 2)
Advances in Pharmacological and Pharmaceutical Sciences     Open Access   (Followers: 8, SJR: 0.591, CiteScore: 2)
Advances in Physical Chemistry     Open Access   (Followers: 13, SJR: 0.179, CiteScore: 1)
Advances in Polymer Technology     Open Access   (Followers: 14, SJR: 0.299, CiteScore: 1)
Advances in Power Electronics     Open Access   (Followers: 44, SJR: 0.184, CiteScore: 0)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 28)
Advances in Regenerative Medicine     Open Access   (Followers: 4)
Advances in Software Engineering     Open Access   (Followers: 11)
Advances in Statistics     Open Access   (Followers: 10)
Advances in Toxicology     Open Access   (Followers: 4)
Advances in Tribology     Open Access   (Followers: 15, SJR: 0.265, CiteScore: 1)
Advances in Urology     Open Access   (Followers: 13, SJR: 0.51, CiteScore: 1)
Advances in Virology     Open Access   (Followers: 8, SJR: 0.838, CiteScore: 2)
AIDS Research and Treatment     Open Access   (Followers: 2, SJR: 0.758, CiteScore: 2)
Analytical Cellular Pathology     Open Access   (Followers: 3, SJR: 0.886, CiteScore: 2)
Anatomy Research Intl.     Open Access   (Followers: 4)
Anemia     Open Access   (Followers: 6, SJR: 0.669, CiteScore: 2)
Anesthesiology Research and Practice     Open Access   (Followers: 15, SJR: 0.501, CiteScore: 1)
Applied and Environmental Soil Science     Open Access   (Followers: 20, SJR: 0.451, CiteScore: 1)
Applied Bionics and Biomechanics     Open Access   (Followers: 7, SJR: 0.288, CiteScore: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 15)
Archaea     Open Access   (Followers: 4, SJR: 0.852, CiteScore: 2)
Autism Research and Treatment     Open Access   (Followers: 36)
Autoimmune Diseases     Open Access   (Followers: 3, SJR: 0.805, CiteScore: 2)
Behavioural Neurology     Open Access   (Followers: 9, SJR: 0.786, CiteScore: 2)
Biochemistry Research Intl.     Open Access   (Followers: 6, SJR: 0.437, CiteScore: 2)
Bioinorganic Chemistry and Applications     Open Access   (Followers: 11, SJR: 0.419, CiteScore: 2)
BioMed Research Intl.     Open Access   (Followers: 5, SJR: 0.935, CiteScore: 3)
Biotechnology Research Intl.     Open Access   (Followers: 1)
Bone Marrow Research     Open Access   (Followers: 2, SJR: 0.531, CiteScore: 1)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 4, SJR: 0.867, CiteScore: 1)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 8, SJR: 0.548, CiteScore: 1)
Canadian Respiratory J.     Open Access   (Followers: 3, SJR: 0.474, CiteScore: 1)
Cardiology Research and Practice     Open Access   (Followers: 11, SJR: 1.237, CiteScore: 4)
Cardiovascular Therapeutics     Open Access   (Followers: 2, SJR: 1.075, CiteScore: 2)
Case Reports in Anesthesiology     Open Access   (Followers: 11)
Case Reports in Cardiology     Open Access   (Followers: 8, SJR: 0.219, CiteScore: 0)
Case Reports in Critical Care     Open Access   (Followers: 12)
Case Reports in Dentistry     Open Access   (Followers: 8, SJR: 0.229, CiteScore: 0)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 19)
Case Reports in Endocrinology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 3)
Case Reports in Genetics     Open Access   (Followers: 2)
Case Reports in Hematology     Open Access   (Followers: 9)
Case Reports in Hepatology     Open Access   (Followers: 2)
Case Reports in Immunology     Open Access   (Followers: 6)
Case Reports in Infectious Diseases     Open Access   (Followers: 6)
Case Reports in Medicine     Open Access   (Followers: 3)
Case Reports in Nephrology     Open Access   (Followers: 5)
Case Reports in Neurological Medicine     Open Access   (Followers: 1)
Case Reports in Obstetrics and Gynecology     Open Access   (Followers: 11)
Case Reports in Oncological Medicine     Open Access   (Followers: 2, SJR: 0.204, CiteScore: 1)
Case Reports in Ophthalmological Medicine     Open Access   (Followers: 3)
Case Reports in Orthopedics     Open Access   (Followers: 6)
Case Reports in Otolaryngology     Open Access   (Followers: 7)
Case Reports in Pathology     Open Access   (Followers: 7)
Case Reports in Pediatrics     Open Access   (Followers: 8)
Case Reports in Psychiatry     Open Access   (Followers: 18)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 12)
Case Reports in Rheumatology     Open Access   (Followers: 10)
Case Reports in Surgery     Open Access   (Followers: 12)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 12)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 5)
Child Development Research     Open Access   (Followers: 21, SJR: 0.144, CiteScore: 0)
Chinese J. of Engineering     Open Access   (Followers: 2, SJR: 0.114, CiteScore: 0)
Chinese J. of Mathematics     Open Access  
Chromatography Research Intl.     Open Access   (Followers: 5)
Complexity     Hybrid Journal   (Followers: 8, SJR: 0.531, CiteScore: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.403, CiteScore: 1)
Computational Biology J.     Open Access   (Followers: 7)
Computational Intelligence and Neuroscience     Open Access   (Followers: 15, SJR: 0.326, CiteScore: 1)
Concepts in Magnetic Resonance Part A     Open Access   (Followers: 1, SJR: 0.354, CiteScore: 1)
Concepts in Magnetic Resonance Part B, Magnetic Resonance Engineering     Open Access   (Followers: 1, SJR: 0.26, CiteScore: 1)
Conference Papers in Science     Open Access   (Followers: 2)
Contrast Media & Molecular Imaging     Open Access   (Followers: 2, SJR: 0.842, CiteScore: 3)
Critical Care Research and Practice     Open Access   (Followers: 13, SJR: 0.499, CiteScore: 1)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 10, SJR: 0.512, CiteScore: 2)
Depression Research and Treatment     Open Access   (Followers: 19, SJR: 0.816, CiteScore: 2)
Dermatology Research and Practice     Open Access   (Followers: 4, SJR: 0.806, CiteScore: 2)
Diagnostic and Therapeutic Endoscopy     Open Access   (SJR: 0.201, CiteScore: 1)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 6, SJR: 0.279, CiteScore: 1)
Disease Markers     Open Access   (Followers: 1, SJR: 0.9, CiteScore: 2)
Economics Research Intl.     Open Access   (Followers: 1)
Education Research Intl.     Open Access   (Followers: 19)
Emergency Medicine Intl.     Open Access   (Followers: 10, SJR: 0.298, CiteScore: 1)
Enzyme Research     Open Access   (Followers: 5, SJR: 0.653, CiteScore: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 30, SJR: 0.683, CiteScore: 2)
Game Theory     Open Access   (Followers: 1)
Gastroenterology Research and Practice     Open Access   (Followers: 1, SJR: 0.768, CiteScore: 2)
Genetics Research Intl.     Open Access   (Followers: 1, SJR: 0.61, CiteScore: 2)
Geofluids     Open Access   (Followers: 5, SJR: 0.952, CiteScore: 2)
Hepatitis Research and Treatment     Open Access   (Followers: 6, SJR: 0.389, CiteScore: 2)
Heteroatom Chemistry     Open Access   (Followers: 3, SJR: 0.333, CiteScore: 1)
HPB Surgery     Open Access   (Followers: 9, SJR: 0.824, CiteScore: 2)
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 5, SJR: 1.27, CiteScore: 2)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 1, SJR: 0.627, CiteScore: 2)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 80, SJR: 0.232, CiteScore: 1)
Intl. J. of Agronomy     Open Access   (Followers: 6, SJR: 0.311, CiteScore: 1)
Intl. J. of Alzheimer's Disease     Open Access   (Followers: 12, SJR: 0.787, CiteScore: 3)
Intl. J. of Analytical Chemistry     Open Access   (Followers: 22, SJR: 0.285, CiteScore: 1)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 13, SJR: 0.233, CiteScore: 1)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 21)
Intl. J. of Biodiversity     Open Access   (Followers: 3)
Intl. J. of Biomaterials     Open Access   (Followers: 5, SJR: 0.511, CiteScore: 2)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 3, SJR: 0.501, CiteScore: 2)
Intl. J. of Breast Cancer     Open Access   (Followers: 14, SJR: 1.025, CiteScore: 2)
Intl. J. of Cell Biology     Open Access   (Followers: 4, SJR: 1.887, CiteScore: 4)
Intl. J. of Chemical Engineering     Open Access   (Followers: 8, SJR: 0.327, CiteScore: 1)
Intl. J. of Chronic Diseases     Open Access   (Followers: 1)
Intl. J. of Combinatorics     Open Access   (Followers: 1)
Intl. J. of Computer Games Technology     Open Access   (Followers: 10, SJR: 0.287, CiteScore: 2)
Intl. J. of Corrosion     Open Access   (Followers: 11, SJR: 0.194, CiteScore: 1)
Intl. J. of Dentistry     Open Access   (Followers: 8, SJR: 0.649, CiteScore: 2)
Intl. J. of Differential Equations     Open Access   (Followers: 8, SJR: 0.191, CiteScore: 0)
Intl. J. of Digital Multimedia Broadcasting     Open Access   (Followers: 5, SJR: 0.296, CiteScore: 2)
Intl. J. of Electrochemistry     Open Access   (Followers: 10)
Intl. J. of Endocrinology     Open Access   (Followers: 4, SJR: 1.012, CiteScore: 3)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 7)
Intl. J. of Food Science     Open Access   (Followers: 5, SJR: 0.44, CiteScore: 2)
Intl. J. of Forestry Research     Open Access   (Followers: 3, SJR: 0.373, CiteScore: 1)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.868, CiteScore: 3)
Intl. J. of Geophysics     Open Access   (Followers: 5, SJR: 0.182, CiteScore: 1)
Intl. J. of Hepatology     Open Access   (Followers: 4, SJR: 0.874, CiteScore: 2)
Intl. J. of Hypertension     Open Access   (Followers: 8, SJR: 0.578, CiteScore: 1)
Intl. J. of Inflammation     Open Access   (SJR: 1.264, CiteScore: 3)
Intl. J. of Inorganic Chemistry     Open Access   (Followers: 4)
Intl. J. of Manufacturing Engineering     Open Access   (Followers: 2)
Intl. J. of Mathematics and Mathematical Sciences     Open Access   (Followers: 3, SJR: 0.177, CiteScore: 0)
Intl. J. of Medicinal Chemistry     Open Access   (Followers: 6, SJR: 0.31, CiteScore: 1)
Intl. J. of Metals     Open Access   (Followers: 7)
Intl. J. of Microbiology     Open Access   (Followers: 8, SJR: 0.662, CiteScore: 2)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 6, SJR: 0.136, CiteScore: 1)
Intl. J. of Navigation and Observation     Open Access   (Followers: 20, SJR: 0.267, CiteScore: 2)
Intl. J. of Nephrology     Open Access   (Followers: 2, SJR: 0.697, CiteScore: 1)
Intl. J. of Oceanography     Open Access   (Followers: 8)
Intl. J. of Optics     Open Access   (Followers: 10, SJR: 0.231, CiteScore: 1)
Intl. J. of Otolaryngology     Open Access   (Followers: 3)
Intl. J. of Partial Differential Equations     Open Access   (Followers: 2)
Intl. J. of Pediatrics     Open Access   (Followers: 6)
Intl. J. of Peptides     Open Access   (Followers: 2, SJR: 0.46, CiteScore: 1)
Intl. J. of Photoenergy     Open Access   (Followers: 3, SJR: 0.341, CiteScore: 1)
Intl. J. of Plant Genomics     Open Access   (Followers: 4, SJR: 0.583, CiteScore: 1)
Intl. J. of Polymer Science     Open Access   (Followers: 28, SJR: 0.298, CiteScore: 1)
Intl. J. of Population Research     Open Access   (Followers: 4)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.123, CiteScore: 1)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 6)
Intl. J. of Rheumatology     Open Access   (Followers: 4, SJR: 0.645, CiteScore: 2)
Intl. J. of Rotating Machinery     Open Access   (Followers: 2, SJR: 0.193, CiteScore: 1)
Intl. J. of Spectroscopy     Open Access   (Followers: 8)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 3, SJR: 0.279, CiteScore: 1)
Intl. J. of Surgical Oncology     Open Access   (Followers: 1, SJR: 0.573, CiteScore: 2)
Intl. J. of Telemedicine and Applications     Open Access   (Followers: 7, SJR: 0.403, CiteScore: 2)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.782, CiteScore: 2)
Intl. J. of Zoology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Intl. Scholarly Research Notices     Open Access   (Followers: 231)

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Similar Journals
Journal Cover
Computational and Mathematical Methods in Medicine
Journal Prestige (SJR): 0.403
Citation Impact (citeScore): 1
Number of Followers: 2  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1748-670X - ISSN (Online) 1748-6718
Published by Hindawi Homepage  [343 journals]
  • DBT Masses Automatic Segmentation Using U-Net Neural Networks

    • Abstract: To improve the automatic segmentation accuracy of breast masses in digital breast tomosynthesis (DBT) images, we propose a DBT mass automatic segmentation algorithm by using a U-Net architecture. Firstly, to suppress the background tissue noise and enhance the contrast of the mass candidate regions, after the top-hat transform of DBT images, a constraint matrix is constructed and multiplied with the DBT image. Secondly, an efficient U-Net neural network is built and image patches are extracted before data augmentation to establish the training dataset to train the U-Net model. And then the presegmentation of the DBT tumors is implemented, which initially classifies per pixel into two different types of labels. Finally, all regions smaller than 50 voxels considered as false positives are removed, and the median filter smoothes the mass boundaries to obtain the final segmentation results. The proposed method can effectively improve the performance in the automatic segmentation of the masses in DBT images. Using the detection Accuracy (Acc), Sensitivity (Sen), Specificity (Spe), and area under the curve (AUC) as evaluation indexes, the Acc, Sen, Spe, and AUC for DBT mass segmentation in the entire experimental dataset is 0.871, 0.869, 0.882, and 0.859, respectively. Our proposed U-Net-based DBT mass automatic segmentation system obtains promising results, which is superior to some classical architectures, and may be expected to have clinical application prospects.
      PubDate: Tue, 28 Jan 2020 15:50:03 +000
  • High-Biofidelity Biomodel Generated from Three-Dimensional Imaging
           (Cone-Beam Computed Tomography): A Methodological Proposal

    • Abstract: Experimental research on living beings faces several obstacles, which are more than ethical and moral issues. One of the proposed solutions to these situations is the computational modelling of anatomical structures. The present study shows a methodology for obtaining high-biofidelity biomodels, where a novel imagenological technique is used, which applies several CAM/CAD computer programs that allow a better precision for obtaining a biomodel, with highly accurate morphological specifications of the molar and tissues that shape the biomodel. The biomodel developed is the first lower molar subjected to a basic chewing simulation through the application of the finite element method, resulting in a viable model, able to be subjected to various simulations to analyse molar biomechanical characteristics, as well as pathological conditions to evaluate restorative materials and develop treatment plans. When research is focused in medical and dental investigation aspects, numerical analyses could allow the implementation of several tools commonly used by mechanical engineers to provide new answers to old problems in these areas. With this methodology, it is possible to perform high-fidelity models no matter the size of the anatomical structure, nor the complexity of its structure and internal tissues. So, it can be used in any area of medicine.
      PubDate: Tue, 28 Jan 2020 13:35:09 +000
  • Automatic Segmentation and Measurement on Knee Computerized Tomography
           Images for Patellar Dislocation Diagnosis

    • Abstract: Traditionally, for diagnosing patellar dislocation, clinicians make manual geometric measurements on computerized tomography (CT) images taken in the knee area, which is often complex and error-prone. Therefore, we develop a prototype CAD system for automatic measurement and diagnosis. We firstly segment the patella and the femur regions on the CT images and then measure two geometric quantities, patellar tilt angle (PTA), and patellar lateral shift (PLS) automatically on the segmentation results, which are finally used to assist in diagnoses. The proposed quantities are proved valid and the proposed algorithms are proved effective by experiments.
      PubDate: Tue, 28 Jan 2020 11:50:05 +000
  • Research of Multimodal Medical Image Fusion Based on Parameter-Adaptive
           Pulse-Coupled Neural Network and Convolutional Sparse Representation

    • Abstract: Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion results due to the loss of detailed feature information during fusion. To deal with it, this paper proposes a new multimodal medical image fusion method based on the imaging characteristics of medical images. In the proposed method, the non-subsampled shearlet transform (NSST) decomposition is first performed on the source images to obtain high-frequency and low-frequency coefficients. The high-frequency coefficients are fused by a parameter‐adaptive pulse-coupled neural network (PAPCNN) model. The method is based on parameter adaptive and optimized connection strength adopted to promote the performance. The low-frequency coefficients are merged by the convolutional sparse representation (CSR) model. The experimental results show that the proposed method solves the problems of difficult parameter setting and poor detail preservation of sparse representation during image fusion in traditional PCNN algorithms, and it has significant advantages in visual effect and objective indices compared with the existing mainstream fusion algorithms.
      PubDate: Fri, 24 Jan 2020 09:35:06 +000
  • Differential Diagnostic Reasoning Method for Benign Paroxysmal Positional
           Vertigo Based on Dynamic Uncertain Causality Graph

    • Abstract: The accurate differentiation of the subtypes of benign paroxysmal positional vertigo (BPPV) can significantly improve the efficacy of repositioning maneuver in its treatment and thus reduce unnecessary clinical tests and inappropriate medications. In this study, attempts have been made towards developing approaches of causality modeling and diagnostic reasoning about the uncertainties that can arise from medical information. A dynamic uncertain causality graph-based differential diagnosis model for BPPV including 354 variables and 885 causality arcs is constructed. New algorithms are also proposed for differential diagnosis through logical and probabilistic inference, with an emphasis on solving the problems of intricate and confounding disease factors, incomplete clinical observations, and insufficient sample data. This study further uses vertigo cases to test the performance of the proposed method in clinical practice. The results point to high accuracy, a satisfactory discriminatory ability for BPPV, and favorable robustness regarding incomplete medical information. The underlying pathological mechanisms and causality semantics are verified using compact graphical representation and reasoning process, which enhance the interpretability of the diagnosis conclusions.
      PubDate: Fri, 24 Jan 2020 02:20:13 +000
  • Multicenter Computer-Aided Diagnosis for Lymph Nodes Using Unsupervised
           Domain-Adaptation Networks Based on Cross-Domain Confounding

    • Abstract: To achieve the robust high-performance computer-aided diagnosis systems for lymph nodes, CT images may be typically collected from multicenter data, which cause the isolated performance of the model based on different data source centers. The variability adaptation problem of lymph node data which is related to the problem of domain adaptation in deep learning differs from the general domain adaptation problem because of the typically larger CT image size and more complex data distributions. Therefore, domain adaptation for this problem needs to consider the shared feature representation and even the conditioning information of each domain so that the adaptation network can capture significant discriminative representations in a domain-invariant space. This paper extracts domain-invariant features based on a cross-domain confounding representation and proposes a cycle-consistency learning framework to encourage the network to preserve class-conditioning information through cross-domain image translations. Compared with the performance of different domain adaptation methods, the accurate rate of our method achieves at least 4.4% points higher under multicenter lymph node data. The pixel-level cross-domain image mapping and the semantic-level cycle consistency provided a stable confounding representation with class-conditioning information to achieve effective domain adaptation under complex feature distribution.
      PubDate: Fri, 24 Jan 2020 02:20:11 +000
  • Ergodic Stationary Distribution of a Stochastic Hepatitis B Epidemic Model
           with Interval-Valued Parameters and Compensated Poisson Process

    • Abstract: Hepatitis B epidemic was and is still a rich subject that sparks the interest of epidemiological researchers. The dynamics of this epidemic is often modeled by a system with constant parameters. In reality, the parameters associated with the Hepatitis B model are not certain, but the interval in which it belongs to can readily be determined. Our paper focuses on an imprecise Hepatitis B model perturbed by Lévy noise due to unexpected environmental disturbances. This model has a global positive solution. Under an appropriate assumption, we prove the existence of a unique ergodic stationary distribution by using the mutually exclusive possibilities lemma demonstrated by Stettner in 1986. Our main effort is to establish an almost perfect condition for the existence of the stationary distribution. Numerical simulations are introduced to illustrate the analytical results.
      PubDate: Wed, 22 Jan 2020 07:50:06 +000
  • Robust Method for Semantic Segmentation of Whole-Slide Blood Cell
           Microscopic Images

    • Abstract: Previous works on segmentation of SEM (scanning electron microscope) blood cell image ignore the semantic segmentation approach of whole-slide blood cell segmentation. In the proposed work, we address the problem of whole-slide blood cell segmentation using the semantic segmentation approach. We design a novel convolutional encoder-decoder framework along with VGG-16 as the pixel-level feature extraction model. The proposed framework comprises 3 main steps: First, all the original images along with manually generated ground truth masks of each blood cell type are passed through the preprocessing stage. In the preprocessing stage, pixel-level labeling, RGB to grayscale conversion of masked image and pixel fusing, and unity mask generation are performed. After that, VGG16 is loaded into the system, which acts as a pretrained pixel-level feature extraction model. In the third step, the training process is initiated on the proposed model. We have evaluated our network performance on three evaluation metrics. We obtained outstanding results with respect to classwise, as well as global and mean accuracies. Our system achieved classwise accuracies of 97.45%, 93.34%, and 85.11% for RBCs, WBCs, and platelets, respectively, while global and mean accuracies remain 97.18% and 91.96%, respectively.
      PubDate: Tue, 21 Jan 2020 04:35:02 +000
  • An Assessment of the Relationship between Structural and Functional
           Imaging of Cerebrovascular Disease and Cognition-Related Fibers

    • Abstract: In order to assess the relationship between structural and functional imaging of cerebrovascular disease and cognition-related fibers, this paper chooses a total of 120 patients who underwent cerebral small vessel disease (CSVD) treatment at a designated hospital by this study from June 2013 to June 2018 and divides them into 3 groups according to the random number table method: vascular dementia (VaD) group, vascular cognitive impairment no dementia (VCIND) group, and noncognition impairment (NCI) group with 40 cases of patients in each group. Cognitive function measurement and imaging examination were performed for these 3 groups of patients, and the observation indicators of cognitive state examination (CSE), mental assessment scale (MAS), clock drawing test (CDT), adult intelligence scale (AIS), frontal assessment battery (FAB), verbal fluency test (VFT), trail making test (TMT), cognitive index (CI), white matter lesions (WML), third ventricle width (TVW), and frontal horn index (FHI) were tested, respectively. The results shows that the average scores of CSE, MAS, AIS, and VFT in the VaD and VCIND group are lower than those of the NCI group and the differences are statistically significant (); the average scores of FAB, TMT, and CI in the VaD group are higher than those of the VCIND group and the differences are also statistically significant (); the average scores of FHI and TVW in the VaD group are lower than those of the VCIND and NCI group with statistically significant differences (); the average scores of WML, CDT, and AIS in the VaD group are higher than those of the VCIND and NCI group with statistically significant differences (). Therefore, it is believed that the structural and functional imaging features of cerebrovascular disease are closely related to cognition-related fibers, and the incidence of white matter lesions is closely related to the degree of lesions and cognitive dysfunction of cerebral small vessel disease, in which a major risk factor for cognitive dysfunction in patients with small blood vessels is the severity of white matter lesions; brain imaging and neuropsychiatric function assessment can better understand the relationship between cerebrovascular disease and cognitive impairment. The results of this study provide a reference for the further research studies on the relationship between structural and functional imaging of cerebrovascular disease and cognition-related fibers.
      PubDate: Mon, 20 Jan 2020 05:35:02 +000
  • Mathematical Prostate Cancer Evolution: Effect of Immunotherapy Based on
           Controlled Vaccination Strategy

    • Abstract: Basic immunology research over several decades has led to an improved understanding of tumour recognition by components of the immune system and mechanism of tumour evasion from immune detection. These findings have ultimately led to creating antitumour immunotherapies in patients with different kind of cancer including prostate cancer. The increasing number of reports confirms that immune-based therapies have clinical benefit in patients with prostate cancer with potentially less toxicity in comparison with traditional systemic treatments including surgical resection, chemotherapy, or radiotherapy in various forms. This review focuses on the possibility of modulation of the optimal immunotherapy based on vaccination strategy adopted to individual patients in order to increase quality and quantity of their life.
      PubDate: Mon, 13 Jan 2020 13:20:02 +000
  • Preoperative Evaluation of V-Y Flap Design Based on Computer-Aided

    • Abstract: V-Y flap is widely used in plastic surgery as an important technique for reconstructing deformities and improving appearance. In this paper, a geometrical parameter model and finite element analysis were used to study the rationale of the proposed V-Y flap design and the preoperative evaluation of the V-Y flap design. First, a geometric parameter model of the V-Y flap was established to analyze the five key geometric relationships affecting the flap structure and obtain a reasonable plan for the V-Y flap design through the crossing constraint relationship. Second, in order to verify the effectiveness of the V-Y flap design, the suture and release states of the V-Y flap during surgery were evaluated based on a simulation model of the V-Y flap generated by finite element analysis software. The results revealed that the approach proposed in this paper provides a feasible method for clinical V-Y flap design.
      PubDate: Sat, 11 Jan 2020 05:50:03 +000
  • Data-Driven Models for Capacity Allocation of Inpatient Beds in a Chinese
           Public Hospital

    • Abstract: Hospital beds are a critical but limited resource shared between distinct classes of elective patients. Urgent elective patients are more sensitive to delays and should be treated immediately, whereas regular patients can wait for an extended time. Public hospitals in countries like China need to maximize their revenue and at the same time equitably allocate their limited bed capacity between distinct patient classes. Consequently, hospital bed managers are under great pressure to optimally allocate the available bed capacity to all classes of patients, particularly considering random patient arrivals and the length of patient stay. To address the difficulties, we propose data-driven stochastic optimization models that can directly utilize historical observations and feature data of capacity and demand. First, we propose a single-period model assuming known capacity; since it recovers and improves the current decision-making process, it may be deployed immediately. We develop a nonparametric kernel optimization method and demonstrate that an optimal allocation can be effectively obtained with one year’s data. Next, we consider the dynamic transition of system state and extend the study to a multiperiod model that allows random capacity; this further brings in substantial improvement. Sensitivity analysis also offers interesting managerial insights. For example, it is optimal to allocate more beds to urgent patients on Mondays and Thursdays than on other weekdays; this is in sharp contrast to the current myopic practice.
      PubDate: Tue, 07 Jan 2020 04:50:05 +000
  • On the Chameleonic Behaviour of Cholesterol through a Fractal/Multifractal

    • Abstract: An increasing number of studies are beginning to show that both low-density lipoprotein and high-density lipoprotein cholesterol can constitute risk factors for myocardial infarction. Such a behaviour has been called by experts in the field the “chameleonic effect” of cholesterol. In the present paper, a fractal/multifractal model for low-density lipoprotein and high-density lipoprotein cholesterol dynamics is proposed. In such a context, a fractal/multifractal tunneling effect for systems with spontaneous symmetry breaking is analyzed so that if the spontaneous symmetry breaking is assimilated to an inflammation (in the form of a specific scalar potential), then a coupling between two fractal/multifractal states can be observed. These two states, which have been associated to biological structures such as low-density lipoprotein and high-density lipoprotein, transfer their states through a fractal/multifractal tunneling effect. Moreover, in our opinion, the widely used notions of “good” and “bad” cholesterol must be redefined as two different states (low-density lipoprotein and high-density lipoprotein) of the same biological structure named “cholesterol.” In our work, for the first time in the specialized literature, low-density lipoprotein and high-density lipoprotein have been regarded as two different states of the same biological structure (named “cholesterol”), such as in nuclear physics, the neutron and proton are two different states of the same particle named nucleon.
      PubDate: Mon, 06 Jan 2020 15:50:03 +000
  • Examining Human Unipedal Quiet Stance: Characterizing Control through Jerk

    • Abstract: We investigated the quality of smoothness during human unipedal quiet stance. Smoothness is quantified by the time rate of change of the accelerations, or jerks, associated with the motion of the foot and can be seen as an indicative of how controlled the balance process is. To become more acquainted with this as a quantity, we wanted to establish whether or not it can be modeled as a (stationary) stochastic process and, if so, explore its temporal scaling behavior. Specifically, our study focused on the jerk concerning the center-of-pressure (COP) for each foot. Data were collected via a force plate for individuals attempting to maintain upright posture using one leg (with eyes open). Positive tests for stochasticity allowed us to treat the time series as a stochastic process and, given this, we took the jerk to be proportional to the increment of the force realizations. Detrended fluctuation analysis was the primary tool used to explore the scaling behavior. Results suggest that both the medial-lateral and anterior-posterior components of the jerk display persistent and antipersistent correlations which can be modeled by fractional Gaussian noise over three different temporal scaling regions. Finally, we discussed certain possible implications of these features such as a jerk-based control over the force on the foot’s COP.
      PubDate: Sat, 04 Jan 2020 13:20:05 +000
  • A Pulse Signal Preprocessing Method Based on the Chauvenet Criterion

    • Abstract: Pulse signals are widely used to evaluate the status of the human cardiovascular, respiratory, and circulatory systems. In the process of being collected, the signals are usually interfered by some factors, such as the spike noise and the poor-sensor-contact noise, which have severely affected the accuracy of the subsequent detection models. In recent years, some methods have been applied to processing the above noisy signals, such as dynamic time warping, empirical mode decomposition, autocorrelation, and cross-correlation. Effective as they are, those methods are complex and difficult to implement. It is also found that the noisy signals are tightly related to gross errors. The Chauvenet criterion, one of the gross error discrimination criterions, is highly efficient and widely applicable for being without the complex calculations like decomposition and reconstruction. Therefore, in this study, based on the Chauvenet criterion, a new pulse signal preprocessing method is proposed, in which adaptive thresholds are designed, respectively, to discriminate the abnormal signals caused by spike noise and poor-sensor-contact noise. 81 hours of pulse signals (with a sleep apnea annotated every 30 seconds and 9,720 segments in total) from the MIT-BIH Polysomnographic Database are used in the study, including 35 minutes of poor-sensor-contact noises and 25 minutes of spike noises. The proposed method was used to preprocess the pulse signals, in which 9,684 segments out of a total of 9,720 were correctly discriminated, and the accuracy of the method reached 99.63%. To quantitatively evaluate the noise removal effect, a simulation experiment is conducted to compare the Jaccard Similarity Coefficient (JSC) calculated before and after the noise removal, respectively, and the results show that the preprocessed signal obtains higher JSC, closer to the reference signal, which indicates that the proposed method can effectively improve the signal quality. In order to evaluate the method, three back-propagation (BP) sleep apnea detection models with the same network structure and parameters were established, respectively. Through comparing the recognition rate and the prediction rate of the models, higher rates were obtained by using the proposed method. To prove the efficiency, the comparison experiment between the proposed Chauvenet-based method and a Romanovsky-based method was conducted, and the execution time of the proposed method is much shorter than that of the Romanovsky method. The results suggest that the superiority in execution time of the Chauvenet-based method becomes more significant as the date size increases.
      PubDate: Mon, 30 Dec 2019 10:50:07 +000
  • Analyzing the Relationship between Cohort and Case-Control Study Results
           Based on Model for Multiple Pathogenic Factors

    • Abstract: Objective. Although the relative risk from a prospective cohort study is numerically approximate to the odds ratio from a case-control study for a low-probability event, a definite relationship between case-control and cohort studies cannot be confirmed. In this study, we established a different model to determine the relationship between case-control and cohort studies. Methods. Two analysis models (the cross-sectional model and multiple pathogenic factor model) were established. Incidences in both the exposure group and the nonexposure group in a cohort study were compared with the frequency of the observed factor in each group (diseased and nondiseased) in a case-control study. Results. The relationship between the results of a case-control study and a cohort study is as follows: ;, where Pe and Pn represent the incidence in the exposed group and nonexposed group, respectively, from the cohort study, while Pd and Pc represent the observed frequencies in the disease group and the control group, respectively, for the case-control study; finally, m represents the incidence in the total population. Conclusions. There is a definite relationship between the results of case-control and cohort studies assessing the same exposure. The outcomes of case-control studies can be translated into cohort study data.
      PubDate: Mon, 30 Dec 2019 10:50:06 +000
  • A Classification Algorithm by Combination of Feature Decomposition and
           Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image
           Classification and AD Diagnosis

    • Abstract: Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegenerative disorder with progressive memory impairment moreover cognitive functions, while MCI would be considered as a transitional phase amongst age-related cognitive weakening. Numerous machine learning approaches have been examined, aiming at AD computer-aided diagnosis through employing MR image analysis. Conversely, MR brain image changes could be caused by different effects such as aging and dementia. It is still a challenging difficulty to extract the relevant imaging features and classify the subjects of different groups. This paper would propose an automatic classification technique based on feature decomposition and kernel discriminant analysis (KDA) for classifications of progressive MCI (pMCI) vs. normal control (NC), AD vs. NC, and pMCI vs. stable MCI (sMCI). Feature decomposition would be based on dictionary learning, which is used for separation of class-specific components from the non-class-specific components in the features, while KDA would be applied for mapping original nonlinearly separable feature space to the separable features that are linear. The proposed technique would be evaluated by employing T1-weighted MR brain images from 830 subjects comprising 198 AD patients, 167 pMCI, 236 sMCI, and 229 NC from the Alzheimer’s disease neuroimaging initiative (ADNI) dataset. Experimental results demonstrate that classification accuracy (ACC) of 90.41%, 84.29%, and 65.94% can be achieved for classification of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, indicating the promising performance of the proposed method.
      PubDate: Mon, 30 Dec 2019 10:35:08 +000
  • Finite Element Modelling of Single Cell Based on Atomic Force Microscope
           Indentation Method

    • Abstract: The stiffness of cells, especially cancer cells, is a key mechanical property that is closely associated with their biomechanical functions, such as the mechanotransduction and the metastasis mechanisms of cancer cells. In light of the low survival rate of single cells and measurement uncertainty, the finite element method (FEM) was used to quantify the deformations and predict the stiffness of single cells. To study the effect of the cell components on overall stiffness, two new FEM models were proposed based on the atomic force microscopy (AFM) indentation method. The geometric sizes of the FEM models were determined by AFM topography images, and the validity of the FEM models was verified by comparison with experimental data. The effect of the intermediate filaments (IFs) and material properties of the cellular continuum components on the overall stiffness were investigated. The experimental results showed that the stiffness of cancer cells has apparent positional differences. The FEM simulation results show that IFs contribute only slightly to the overall stiffness within 10% strain, although they can transfer forces directly from the membrane to the nucleus. The cytoskeleton (CSK) is the major mechanical component of a cell. Furthermore, parameter studies revealed that the material properties (thickness and elasticity) of the continuum have a significant influence on the overall cellular stiffness while Poisson’s ratio has less of an influence on the overall cellular stiffness. The proposed FEM models can determine the contribution of the major components of the cells to the overall cellular stiffness and provide insights for understanding the response of cells to the external mechanical stimuli and studying the corresponding mechanical mechanisms and cell biomechanics.
      PubDate: Fri, 20 Dec 2019 10:20:13 +000
  • Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial
           Constitution Classification

    • Abstract: Constitution classification is the basis and core content of TCM constitution research. In order to improve the accuracy of constitution classification, this paper proposes a multilevel and multiscale features aggregation method within the convolutional neural network, which consists of four steps. First, it uses the pretrained VGG16 as the basic network and then refines the network structure through supervised feature learning so as to capture local image features. Second, it extracts the image features of different layers from the fine-tuned VGG16 model, which are then dimensionally reduced by principal component analysis (PCA). Third, it uses another pretrained NASNetMobile network for supervised feature learning, where the previous layer features of the global average pooling layer are outputted. Similarly, these features are dimensionally reduced by PCA and then are fused with the features of different layers in VGG16 after the PCA. Finally, all features are aggregated with the fully connected layers of the fine-tuned VGG16, and then the constitution classification is performed. The conducted experiments show that using the multilevel and multiscale feature aggregation is very effective in the constitution classification, and the accuracy on the test dataset reaches 69.61%.
      PubDate: Fri, 20 Dec 2019 10:20:12 +000
  • Gait Biomarkers Classification by Combining Assembled Algorithms and Deep
           Learning: Results of a Local Study

    • Abstract: Machine learning, one of the core disciplines of artificial intelligence, is an approach whose main emphasis is analytical model building. In other words, machine learning enables an automaton to make its own decisions based on a previous training process. Machine learning has revolutionized every research sector, including health care, by providing precise and accurate decisions involving minimal human interventions through pattern recognition. This is emphasized in this research, which addresses the issue of “support for diabetic neuropathy (DN) recognition.” DN is a disease that affects a large proportion of the global population. In this research, we have used gait biomarkers of subjects representing a particular sector of population located in southern Mexico to identify persons suffering from DN. To do this, we used a home-made body sensor network to capture raw data of the walking pattern of individuals with and without DN. The information was then processed using three sampling criteria and 23 assembled classifiers, in combination with a deep learning algorithm. The architecture of the best combination was chosen and reconfigured for better performance. The results revealed a highly acceptable classification with greater than 85% accuracy when using these combined approaches.
      PubDate: Thu, 19 Dec 2019 08:05:01 +000
  • Effects of Different Positions and Angles of Implants in Maxillary
           Edentulous Jaw on Surrounding Bone Stress under Dynamic Loading: A
           Three-Dimensional Finite Element Analysis

    • Abstract: Purpose. To evaluate the effects of different placements of mesial implants and different angles of distant implants in maxillary edentulous jaws on the stress on the implant and the surrounding bone tissue under dynamic loading. Materials and Methods. Cone beam computed tomography was used to acquire images of maxillary edentulous jaws. Using Mimics 17.0, Geomagic, and Unigraphics NX8.5 software, three-dimensional models were established: two mesial implants were placed vertically in the anterior region of the maxilla (bilateral central incisor, lateral incisor, and canine), and two distant implants were placed obliquely in the bilateral second premolar area at different inclined angles (15°, 30°, and 45°). The established models were designated I–IX. The models were subjected to dynamic load using Abaqus 6.12, with the working side posterior teeth loading of 150 N and simulation cycle of 0.875 s. Results. During the second to fourth phases of the mastication cycle, the stress was mainly concentrated on the neck of the distal implant. The stress of the distal implants was greater than that of mesial implants. Stress levels peaked in the third stage of the cycle. The stress of the distal cortical bone of distal implant of Model I reached the maximum of 183.437 MPa. The stress of the distal cortical bone and cancellous bone of distal implant of Model VIII represented the minima (62.989 MPa and 17.186 MPa, respectively). Conclusions. Our models showed optimal stress reductions when the mesial implants were located in the canine region and the distal implants tilted 30°.
      PubDate: Tue, 17 Dec 2019 03:50:04 +000
  • SVR-EEMD: An Improved EEMD Method Based on Support Vector Regression
           Extension in PPG Signal Denoising

    • Abstract: Photoplethysmography (PPG) has been widely used in noninvasive blood volume and blood flow detection since its first appearance. However, its noninvasiveness also makes the PPG signals vulnerable to noise interference and thus exhibits nonlinear and nonstationary characteristics, which have brought difficulties for the denoising of PPG signals. Ensemble empirical mode decomposition known as EEMD, which has made great progress in noise processing, is a noise-assisted nonlinear and nonstationary time series analysis method based on empirical mode decomposition (EMD). The EEMD method solves the “mode mixing” problem in EMD effectively, but it can do nothing about the “end effect,” another problem in the decomposition process. In response to this problem, an improved EEMD method based on support vector regression extension (SVR-EEMD) is proposed and verified by simulated data and real-world PPG data. Experiments show that the SVR-EEMD method can solve the “end effect” efficiently to get a better decomposition performance than the traditional EEMD method and bring more benefits to the noise processing of PPG signals.
      PubDate: Thu, 12 Dec 2019 04:20:08 +000
  • Unpaired Low-Dose CT Denoising Network Based on Cycle-Consistent
           Generative Adversarial Network with Prior Image Information

    • Abstract: The widespread application of X-ray computed tomography (CT) in clinical diagnosis has led to increasing public concern regarding excessive radiation dose administered to patients. However, reducing the radiation dose will inevitably cause server noise and affect radiologists’ judgment and confidence. Hence, progressive low-dose CT (LDCT) image reconstruction methods must be developed to improve image quality. Over the past two years, deep learning-based approaches have shown impressive performance in noise reduction for LDCT images. Most existing deep learning-based approaches usually require the paired training dataset which the LDCT images correspond to the normal-dose CT (NDCT) images one-to-one, but the acquisition of well-paired datasets requires multiple scans, resulting the increase of radiation dose. Therefore, well-paired datasets are not readily available. To resolve this problem, this paper proposes an unpaired LDCT image denoising network based on cycle generative adversarial networks (CycleGAN) with prior image information which does not require a one-to-one training dataset. In this method, cyclic loss, an important trick in unpaired image-to-image translation, promises to map the distribution from LDCT to NDCT by using unpaired training data. Furthermore, to guarantee the accurate correspondence of the image content between the output and NDCT, the prior information obtained from the result preprocessed using the LDCT image is integrated into the network to supervise the generation of content. Given the map of distribution through the cyclic loss and the supervision of content through the prior image loss, our proposed method can not only reduce the image noise but also retain critical information. Real-data experiments were carried out to test the performance of the proposed method. The peak signal-to-noise ratio (PSNR) improves by more than 3 dB, and the structural similarity (SSIM) increases when compared with the original CycleGAN without prior information. The real LDCT data experiment demonstrates the superiority of the proposed method according to both visual inspection and quantitative evaluation.
      PubDate: Sat, 07 Dec 2019 04:50:05 +000
  • Segmented Linear Regression Modelling of Time-Series of Binary Variables
           in Healthcare

    • Abstract: Introduction. In healthcare, change is usually detected by statistical techniques comparing outcomes before and after an intervention. A common problem faced by researchers is distinguishing change due to secular trends from change due to an intervention. Interrupted time-series analysis has been shown to be effective in describing trends in retrospective time-series and in detecting change, but methods are often biased towards the point of the intervention. Binary outcomes are typically modelled by logistic regression where the log-odds of the binary event is expressed as a function of covariates such as time, making model parameters difficult to interpret. The aim of this study was to present a technique that directly models the probability of binary events to describe change patterns using linear sections. Methods. We describe a modelling method that fits progressively more complex linear sections to the time-series of binary variables. Model fitting uses maximum likelihood optimisation and models are compared for goodness of fit using Akaike’s Information Criterion. The best model describes the most likely change scenario. We applied this modelling technique to evaluate hip fracture patient mortality rate for a total of 2777 patients over a 6-year period, before and after the introduction of a dedicated hip fracture unit (HFU) at a Level 1, Major Trauma Centre. Results. The proposed modelling technique revealed time-dependent trends that explained how the implementation of the HFU influenced mortality rate in patients sustaining proximal femoral fragility fractures. The technique allowed modelling of the entire time-series without bias to the point of intervention. Modelling the binary variable of interest directly, as opposed to a transformed variable, improved the interpretability of the results. Conclusion. The proposed segmented linear regression modelling technique using maximum likelihood estimation can be employed to effectively detect trends in time-series of binary variables in retrospective studies.
      PubDate: Fri, 06 Dec 2019 04:50:03 +000
  • Methodology Proposal of EMG Hand Movement Classification Based on Cross
           Recurrence Plots

    • Abstract: Dealing with electromyography (EMG) signals is often not simple. The nature of these signals is nonstationary, noisy, and high dimensional. These EMG characteristics make their predictability even more challenging. Cross recurrence plots (CRPs) have demonstrated in many works their capability of detecting very subtle patterns in signals often buried in a noisy environment. In this contribution, fifty subjects performed ten different hand movements with each hand with the aid of electrodes placed in each arm. Furthermore, the nonlinear features of each subject’s signals using cross recurrence quantification analysis (CRQA) have been performed. Also, a novel methodology is proposed using CRQA as the mainstream technique to detect and classify each of the movements presented in this study. Additional tools were presented to determine to which extent this proposed methodology is able to avoid false classifications, thus demonstrating that this methodology is feasible to classify surface EMG (SEMG) signals with good accuracy, sensitivity, and specificity. Lastly, the results were compared with traditional machine learning methods, and the advantages of using the proposed methodology above such methods are highlighted.
      PubDate: Wed, 04 Dec 2019 14:20:06 +000
  • Green Fluorescent Protein and Phase-Contrast Image Fusion via Generative
           Adversarial Networks

    • Abstract: In the field of cell and molecular biology, green fluorescent protein (GFP) images provide functional information embodying the molecular distribution of biological cells while phase-contrast images maintain structural information with high resolution. Fusion of GFP and phase-contrast images is of high significance to the study of subcellular localization, protein functional analysis, and genetic expression. This paper proposes a novel algorithm to fuse these two types of biological images via generative adversarial networks (GANs) by carefully taking their own characteristics into account. The fusion problem is modelled as an adversarial game between a generator and a discriminator. The generator aims to create a fused image that well extracts the functional information from the GFP image and the structural information from the phase-contrast image at the same time. The target of the discriminator is to further improve the overall similarity between the fused image and the phase-contrast image. Experimental results demonstrate that the proposed method can outperform several representative and state-of-the-art image fusion methods in terms of both visual quality and objective evaluation.
      PubDate: Wed, 04 Dec 2019 06:35:03 +000
  • The Use of Arabic Vowels to Model the Pathological Effect of Influenza
           Disease by Wavelets

    • Abstract: Speech parameters may include perturbation measurements, spectral and cepstral modeling, and pathological effects of some diseases, like influenza, that affect the vocal tract. The verification task is a very good process to discriminate between different types of voice disorder. This study investigated the modeling of influenza’s pathological effects on the speech signals of the Arabic vowels “A” and “O.” For feature extraction, linear prediction coding (LPC) of discrete wavelet transform (DWT) subsignals denoted by LPCW was used. k-Nearest neighbor (KNN) and support vector machine (SVM) classifiers were used for classification. To study the pathological effects of influenza on the vowel “A” and vowel “O,” power spectral density (PSD) and spectrogram were illustrated, where the PSD of “A” and “O” was repressed as a result of the pathological effects. The obtained results showed that the verification parameters achieved for the vowel “A” were better than those for vowel “O” for both KNN and SVM for an average. The receiver operating characteristic curve was used for interpretation. The modeling by the speech utterances as words was also investigated. We can claim that the speech utterances as words could model the influenza disease with a good quality of the verification parameters with slightly less performance than the vowels “A” as speech utterances. A comparison with state-of-the-art method was made. The best results were achieved by the LPCW method.
      PubDate: Wed, 04 Dec 2019 05:50:05 +000
  • Biases in the Simulation and Analysis of Fractal Processes

    • Abstract: Fractal processes have recently received a growing interest, especially in the domain of rehabilitation. More precisely, the evolution of fractality with aging and disease, suggesting a loss of complexity, has inspired a number of studies that tried, for example, to entrain patients with fractal rhythms. This kind of study requires relevant methods for generating fractal signals and for assessing the fractality of the series produced by participants. In the present work, we engaged a cross validation of three methods of generation and three methods of analysis. We generated exact fractal series with the Davies–Harte (DH) algorithm, the spectral synthesis method (SSM), and the ARFIMA simulation method. The series were analyzed by detrended fluctuation analysis (DFA), power spectral density (PSD) method, and ARFIMA modeling. Results show that some methods of generation present systematic biases: DH presented a strong bias toward white noise in fBm series close to the 1/f boundary and SSM produced series with a larger variability around the expected exponent, as compared with other methods. In contrast, ARFIMA simulations provided quite accurate series, without major bias. Concerning the methods of analysis, DFA tended to systematically underestimate fBm series. In contrast, PSD yielded overestimates for fBm series. With DFA, the variability of estimates tended to increase for fGn series as they approached the 1/f boundary and reached unacceptable levels for fBm series. The highest levels of variability were produced by PSD. Finally, ARFIMA methods generated the best series and provided the most accurate and less variable estimates.
      PubDate: Tue, 03 Dec 2019 05:05:07 +000
  • Differentiation of Recurrence from Radiation Necrosis in Gliomas Based on
           the Radiomics of Combinational Features and Multimodality MRI Images

    • Abstract: Purpose. To classify radiation necrosis versus recurrence in glioma patients using a radiomics model based on combinational features and multimodality MRI images. Methods. Fifty-one glioma patients who underwent radiation treatments after surgery were enrolled in this study. Sixteen patients revealed radiation necrosis while 35 patients showed tumor recurrence during the follow-up period. After treatment, all patients underwent T1-weighted, T1-weighted postcontrast, T2-weighted, and fluid-attenuated inversion recovery scans. A total of 41,284 handcrafted and 24,576 deep features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (denoted as 0.632 + bootstrap AUC) metric were used to select the features. The stepwise forward method was applied to construct 10 logistic regression models based on different combinations of image features. Results. For handcrafted features on multimodality MRI, model 7 with seven features yielded the highest AUC of 0.9624, sensitivity of 0.8497, and specificity of 0.9083 in the validation set. These values were higher than the accuracy of using handcrafted features on single-modality MRI (paired t-test, , except sensitivity). For combined handcrafted and AlexNet features on multimodality MRI, model 6 with six features achieved the highest AUC of 0.9982, sensitivity of 0.9941, and specificity of 0.9755 in the validation set. These values were higher than the accuracy of using handcrafted features on multimodality MRI (paired t-test, ).Conclusions. Handcrafted and deep features extracted from multimodality MRI images reflecting the heterogeneity of gliomas can provide useful information for glioma necrosis/recurrence classification.
      PubDate: Sun, 01 Dec 2019 15:05:18 +000
  • Simulation Study on the Mass Transport Based on the Ciliated Dynamic
           System of the Respiratory Tract

    • Abstract: To study the mass transport of mucociliary clearance of the human upper respiratory tract, a two-dimensional mass transport model based on the ciliated movement was established by using the immersed boundary-lattice Boltzmann method (IB-LBM). In this model, different characteristics of the mucus layer (ML) and the periciliary liquid (PCL) were taken into account. A virtual elastic membrane was introduced to divide the two layers dynamically. All moving boundaries that were involved in the present simulation were modeled with the immersed boundary. The Newtonian fluid was used to model the flow in PCL, and the viscoelastic fluid based on the Oldroyd-B model was used for the flow in ML; the two types of flow were both solved by the LBM framework. Based on the model, the ML thickness, the cilia density, and the phase difference of adjacent cilia were regulated, respectively, to study the transport velocity of the ML. In addition, the motion law of solid particles in PCL was also studied. According to the results, four primary conclusions were drawn. (1) At a given beating pattern, the increase of the ML thickness will decrease its transport velocity. (2) Increasing the cilia density can promote the mean transport velocity of the ML. (3) By raising the phase difference of adjacent cilia to a certain scope, the transport of ML can be accelerated. (4) In PCL, particles initially located on the upper part of the cilia tend to migrate upward and then get close to the ML. The above study can provide some reasonable explanations for the mechanism of the mucociliary clearance system, which is also helpful to the further understanding of the mass transport principle of the human upper respiratory tract.
      PubDate: Wed, 27 Nov 2019 11:05:15 +000
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