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BIOTECHNOLOGY (236 journals)                  1 2 | Last

Showing 1 - 200 of 239 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 8)
Advanced Biomedical Research     Open Access  
Advances in Bioscience and Biotechnology     Open Access   (Followers: 16)
Advances in Genetic Engineering & Biotechnology     Hybrid Journal   (Followers: 7)
Advances in Regenerative Medicine     Open Access   (Followers: 2)
African Journal of Biotechnology     Open Access   (Followers: 6)
Algal Research     Partially Free   (Followers: 11)
American Journal of Biochemistry and Biotechnology     Open Access   (Followers: 67)
American Journal of Bioinformatics Research     Open Access   (Followers: 7)
American Journal of Polymer Science     Open Access   (Followers: 32)
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 Biosafety     Hybrid Journal  
Applied Food Biotechnology     Open Access   (Followers: 3)
Applied Microbiology and Biotechnology     Hybrid Journal   (Followers: 64)
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: 9)
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)
Beitr?ge zur Tabakforschung International/Contributions to Tobacco Research     Open Access   (Followers: 3)
Bio-Algorithms and Med-Systems     Hybrid Journal   (Followers: 2)
Bio-Research     Full-text available via subscription   (Followers: 3)
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   (Followers: 1)
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)
Biosensors Journal     Open Access  
Biosimilars     Open Access   (Followers: 1)
Biosurface and Biotribology     Open Access  
Biotechnic and Histochemistry     Hybrid Journal   (Followers: 1)
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: 6)
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: 2)
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: 40)
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  
BIOTIK : Jurnal Ilmiah Biologi Teknologi dan Kependidikan     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)
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)
Indonesian Journal of Medicine     Open Access  
Industrial Biotechnology     Hybrid Journal   (Followers: 17)
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: 3)
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)
JMIR Biomedical Engineering     Open Access  
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: 64)
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   (Followers: 1)
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: 25)
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: 17)
Journal of Integrative Bioinformatics     Open Access  
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: 12)
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)
Meat Technology     Open Access  
Messenger     Full-text available via subscription  
Metabolic Engineering Communications     Open Access   (Followers: 4)
Metalloproteinases In Medicine     Open Access  
Microbial Biotechnology     Open Access   (Followers: 9)
MicroMedicine     Open Access   (Followers: 3)
Molecular and Cellular Biomedical Sciences     Open Access   (Followers: 1)
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  
Nanomedicine and Nanobiology     Full-text available via subscription  
Nanomedicine Research Journal     Open Access  

        1 2 | Last

Journal Cover
Biocybernetics and Biological Engineering
Journal Prestige (SJR): 0.384
Citation Impact (citeScore): 2
Number of Followers: 5  
  Full-text available via subscription Subscription journal
ISSN (Print) 0208-5216
Published by Elsevier Homepage  [3161 journals]
  • Statistical methods for constructing gestational age-related charts for
           fetal size and pregnancy dating using longitudinal data
    • Abstract: Publication date: Available online 18 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Martin Hynek, Jan Kalina, Jana Zvárová, Jeffrey D. Long The assessment of fetal size and the accurate estimation of gestational age are of crucial importance for proper pregnancy management. The information is almost exclusively based on ultrasound measurements of fetal biometric parameters and the means for evaluating these measurements are age-related reference charts (centile charts) allowing interpretation of obtained fetal measurement in comparison with the expected average measurement in the reference population. The construction of such reference charts requires an appropriate statistical methodology. The most frequent method for the construction of fetal reference charts from cross-sectional data is the parametric approach with fractional polynomials regression functions for the mean and standard deviation of each fetal measurement. This article suggests how this method can be extended to longitudinal data using fractional polynomials in linear mixed effect regression. The presented approach includes maximum likelihood estimation for fitting first- and second-order fractional polynomial models, and multimodel inference using Akaike's information criterion and related tools as a suitable strategy for model selection. Finally, an example of the suggested approach is presented.
  • Evaluation of filters over different stimulation models in evoked
    • Abstract: Publication date: Available online 17 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Ayşegül Güven, Turgay Batbat Filtering is a key process which removes unwanted parts of signals. During signal recording, various forms of noises distort data. Physiological signals are highly noise sensitive and to evaluate them powerful filtering approaches must be applied. The aim of this study is to compare modern filtering approaches on scalp signals. Brain activities were generally examined by brain signals like EEG and evoked potentials (EP). In this study, data were recorded from university students whose age between 18 and 25 years with visual and auditory stimuli. Discrete wavelet transforms, singular spectrum analysis, empirical mode decomposition and discrete Fourier transform based filters were used and compared with raw data on classification performance. Higuchi fractal dimension and entropy features were extracted from EEG; P300 features were extracted from EP signals. Classification was applied with support vector machines. All filtered data gave better scores than raw data. Empirical mode decomposition (EMD) and Fourier-based filter yielded lower results than the discrete wavelet-based filter. Singular spectrum analysis gave the best result at 84.32%. The current study suggests that singular spectrum analysis removes noise from sensitive physiological signals, and EMD requires new mode selection procedures before resynthesizing.
  • Fuzzy genetic-based noise removal filter for digital panoramic X-ray
    • Abstract: Publication date: Available online 5 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Mehravar Rafati, Fateme Farnia, Mahdi Erfanian Taghvaei, Ali Mohammad Nickfarjam This paper proposed a novel fuzzy genetic-based noise removal filter and surveyed the gain of popular filters for noise removal in the digital orthopantomography (OPG) images. The proposed filter is a non-invasive technique for attaining sub-clinical information from the areas of interest in each tooth, both jaws and maxillofacial.The proposed Poisson removal filter combines 4th-order partial differential equations (PDE), total variation (TV) and Bayes shrink threshold accompanied by fuzzy genetic algorithm (FGA) and the exact unbiased inverse of generalized Anscombe transformation (EUIGAT). Experiments were performed in order to show the effect of noise removal filters on 110 simulated, 106 phantom and 104 panoramic radiographic images for subjects (aged 30–60 years old, 50 males and 54 females). Various noises degraded filters and Canny edge detection was performed separately in three kinds of images. The program measured mean square error (MSE), peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index metric (SSIM) and figure of merit (FOM).The results verify that the proposed filter enhances physicians’ and dentists’ skill of diagnosing normal and pathological events in the teeth, jaws, temporomandibular joint (TMJ) regions and changeable anatomical panoramic landmarks related to osteoporosis progress in the mandible bone using noise removal and improving images quality. Experimental results show the superiority of this filter over other noise removal filters.
  • A hybrid gene selection method for microarray recognition
    • Abstract: Publication date: Available online 5 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Alok Kumar Shukla, Pradeep Singh, Manu Vardhan DNA microarray data is expected to be a great help in the development of efficient diagnosis and tumor classification. However, due to the small number of instances compared to a large number of genes, many of the computational learning methods encounter difficulties to select the low subgroups. In order to select significant genes from the high dimensional data for tumor classification, nowadays, several researchers are exploring microarray data using various gene selection methods. However, there is no agreement between existing gene selection techniques that produce the relevant gene subsets by which it improves the classification accuracy. This motivates us to invent a new hybrid gene selection method which helps to eliminate the misleading genes and classify a disease correctly in less computational time. The proposed method composes of two-stage, in the first stage, EGS method using multi-layer approach and f-score approach is applied to filter the noisy and redundant genes from the dataset. In the second stage, adaptive genetic algorithm (AGA) work as a wrapper to identify significant genes subsets from the reduced datasets produced by EGS that can contribute to detect cancer or tumor. AGA algorithm uses the support vector machine (SVM) and Naïve Bayes (NB) classifier as a fitness function to select the highly discriminating genes and to maximize the classification accuracy. The experimental results show that the proposed framework provides additional support to a significant reduction of cardinality and outperforms the state-of-art gene selection methods regarding accuracy and an optimal number of genes.
  • Towards in-vivo assessment of fluorescence lifetime: Imaging using
           time-gated intensified CCD camera
    • Abstract: Publication date: Available online 3 September 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Piotr Sawosz, Stanislaw Wojtkiewicz, Michal Kacprzak, Elzbieta Zieminska, Magdalena Morawiec, Roman Maniewski, Adam Liebert A novel technique for imaging of a small animal with application of time-gated intensified CCD camera was proposed. The time-resolved method based on emission of picosecond light pulses and detection of the light penetrating in tissues was applied. In this technique, the fluorescence photons, excited in the dye circulating in the tissue, that diffusely penetrate in the optically turbid medium are detected. The data acquired during measurements carried out on a rat was analyzed in order to estimate fluorescence lifetime which depends strongly on the environment in which the dye is distributed. In the lifetime estimation a special emphasis was put on compensation of influence of the instrumental response function of the setup on the measured quantity. The proposed optical system was validated in series phantom experiments, in which estimates of fluorescence lifetime of inclusions containing indocyanine green (ICG) were obtained. ICG is a dye revealing florescence properties in near-infrared wavelength region. Images of the estimate of fluorescence lifetime of the ICG accumulated in tissues of a rat were successfully acquired around six circular spots of illumination of the diameter of 6 mm. Larger lifetime values were observed in lung/heart region of the animal. Aspect of sampling rate of the fluorescence lifetime images optimization was finally discussed.
  • Fast statistical model-based classification of epileptic EEG signals
    • Abstract: Publication date: Available online 21 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Antonio Quintero-Rincón, Marcelo Pereyra, Carlos D’Giano, Marcelo Risk, Hadj Batatia This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using a wavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature.
  • Extracting tumor in MR brain and breast image with Kapur’s entropy based
           Cuckoo Search Optimization and morphological reconstruction filters
    • Abstract: Publication date: Available online 20 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): R. Sumathi, M. Venkatesuslu, Sridhar P. Arjunan
  • Automated fuzzy optic disc detection algorithm using branching of vessels
           and color properties in fundus images
    • Abstract: Publication date: Available online 20 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Mehmet Nergiz, Mehmet Akın, Abdulnasır Yıldız, Ömer Takeş Optic disc (OD) detection is a basic procedure for the image processing algorithms which intend to diagnose and track retinal disorders. In this study, a new OD localization approach is proposed, based on color and shape properties of OD as well as the convergence point of the main vessels. This study is comprised of two successive fundamental steps. At the first step, an algorithm finding the approximate convergent point of the vessels is used in order to roughly localize OD. At the second step, three new features are suggested and a fuzzy logic controller (FLC) whose input membership functions are designed based on these features is proposed. The proposed method is applied to the DRIVE, STARE, DIARETDB0 and DIRETDB1 datasets and the obtained results validate the improvement in the performance by attaining success rate of 100%, 91,35%, 90% and 100% respectively and detecting OD centers and contours precisely in a reasonable execution time.
  • Eye and EEG activity markers for visual comfort level of images
    • Abstract: Publication date: Available online 13 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Vytautas Abromavičius, Artūras Serackis Depth perception by binocular cues is based on the matching of image features from one retina with corresponding elements from the second retina. However, high disparities are related to the higher visual discomfort levels and may cause the eye fatigue during extended stereoscopic perception time. The goal of the investigation was to find a set of measurable features for stereoscopic image visual comfort level prediction. The investigation involved gaze, pupillometric and EEG data from 28 subjects who evaluated visual comfort level of 120 stereoscopic images. Six different time frame windows were used to analyze four measured features: the number of focus points; the dynamics of pupil size; disparity level at the focus points; the activity of EEG bands at the frontal lobe. A significant difference was found in all investigated stereoscopic image groups. 2-s and 5-s pre-DPI window showed best results for the selected feature sets. The higher disparity at the focus points, lower number of focus points are related to the lower levels of visual comfort. However, features such as the number of focus points, the pupil size and the disparity level for the images with lowest visual comfort scores showed similar results to the images scored as “comfortable” or “very comfortable”.
  • A miniature and low-cost glucose measurement system
    • Abstract: Publication date: Available online 8 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): S.D. Adams, E. Buber, T.C. Bicak, Y. Yagci, L. Toppare, A. Kaynak, A.Z. Kouzani One of the bottlenecks in widespread adoption of biosensors is the large and sophisticated bioanalytical system that is required to perform signal transduction and analysis. A miniaturized bioanalytical system facilitates biosensing techniques that are portable, easy to handle and inexpensive for fast and reliable measurements of biochemical species. Thus, downscaling the bioanalytical system has become a highly active research area, significantly assisted by recent advances in the microelectronics technology. In this work, a miniaturized system is designed and implemented for amperometric detection, and subsequently tested with a glucose biosensor based on the one-step approach utilizing water soluble poly(o-aminophenol). Several experiments are conducted to assess the viability of this system including calibration, interference and application tests. The results are compared with the previously published work performed using the same biosensor tested with a commercial potentiostat in order to verify the applicability of the designed system.
  • RASIT: Region shrinking based accurate segmentation of inflammatory areas
           from thermograms
    • Abstract: Publication date: Available online 3 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Bardhana Shawli, Kanti Bhowmika Mrinal, Debnatha Tathagata, Debotosh Bhattacharjee Effective segmentation of thermal images reflecting the inflamed region in human body to assist medical diagnosis is a challenging task. In this paper we propose a method for thermal image segmentation, named as “Region shrinking based Accurate Segmentation of Inflammatory areas from Thermograms”, in short RASIT. The method comprising of four steps encompassing thermal image contextual electrostatic force extraction, intensity adjustment as applicable, automated generation of the weighted threshold, and segmentation of thermograms based on the computed threshold. The proposed method is operative devoid of the subjective and possibly questionable task of parameter selection clearly offering an edge over the state-of-the-art methods in terms of usage. The efficacy of our proposed technique is shown by experimenting on abnormal thermograms taken from two datasets: one is newly created knee arthritis thermogram dataset and another is online available Database of Mastology Research (DMR) of breast thermograms. The averages on correct detection rates obtained by the proposed method for both the knee and breast thermograms are 98.2% and 96.98% respectively with favorable inference on basis of Wilcoxon’s test. Application of the proposed method minimizes the complexity of parameter selection, time complexity of execution and amount of under segmentation compared to existing state-of-the-art methods of thermogram segmentation.
  • Modeling the 2D space of emotions based on the poincare plot of heart rate
           variability signal
    • Abstract: Publication date: Available online 3 August 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Sadaf Moharreri, Nader Jafarnia Dabanloo, Keivan Maghooli Emotions mean accepting, understanding, and recognizing something with one's senses. The physiological signals generated from the internal organs of the body can objectively and realistically reflect changes in real-time human emotions and monitor the state of the body. In this study, the two-dimensional space-based emotion model was introduced on the basis of Poincare's two-dimensional plot of the signal of heart rate variability. Four main colors of psychology, blue, red, green, and yellow were used as a stimulant of emotion, and the ECG signals from 70 female students were recorded. Using extracted features of Poincare plot and heart rate asymmetry, two tree based models estimated the levels of arousal and valence with 0.05 mean square errors, determined an appropriate estimation of these two parameters of emotion. In the next stage of the study, four different emotions mean pleasure, anger, joy, and sadness, were classified using IF-THEN rules with the accuracy of 95.71%. The results show the color red is associated with more excitement and anger, while green has small anxiety. So, this system provides a measure for numerical comparison of mental states and makes it possible to model emotions for interacting with the computer and control mental states independently of the pharmaceutical methods.
  • Formulation and statistical evaluation of an automated algorithm for
           locating small bowel tumours in wireless capsule endoscopy
    • Abstract: Publication date: Available online 31 July 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): A. Jagadeesan, J. Sivaraman Wireless capsule endoscopy (WCE) is an imaging modality which is highly reliable in the diagnosis of small bowel tumors. But locating the frames carrying tumors manually from the lengthy WCE is cumbersome and time consuming. A simple algorithm for the automated detection of tumorous frames from WCE is proposed in this work. In the proposed algorithm, local binary pattern (LBP) of the contrast enhanced green channel is used as the textural descriptor of the WCE frames. The features employed to differentiate tumorous and non-tumorous frames are skewness (S) and kurtosis (K) of the LBP histogram. The threshold value of the features which offers the trade-off between sensitivity and specificity is identified through Receiver Operating Characteristic (ROC) curve analysis. At the optimum threshold, both the features exhibited a sensitivity of 100% and specificity of 90%. The skewness and kurtosis of the LBP computed from the enhanced green channel of tumorous and non-tumorous frames differ significantly (p « 0.05) with a p-value of 2.2 × 10−16. The proposed method is helpful to reduce the time spent by the doctors for reviewing WCE.
  • Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face
    • Abstract: Publication date: Available online 27 July 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Krzysztof Kotowski, Katarzyna Stapor, Jacek Leski, Marian Kotas The article presents our proposed adaptation of the commercially available Emotiv EPOC+ EEG headset for neuroscience research based on event-related brain potentials (ERP). It solves Emotiv EPOC+ synchronization problems (common to most low-cost systems) by applying our proposed stimuli marking circuit. The second goal was to check the capabilities of our modification in neuroscience experiments on emotional face processing. Results of our experiment show the possibility of measuring small differences in the early posterior negativity (EPN) component between neutral and emotional (angry/happy) stimuli consistently with previous works using research-grade EEG systems.
  • Predicting the success of wart treatment methods using decision tree based
           fuzzy informative images
    • Abstract: Publication date: Available online 17 July 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Selahaddin Batuhan Akben Warts are small, rough, benign tumours caused by human papillomavirus (HPV). A challenge is predicting the success of wart treatment methods because success may vary depending on the patient and the features of disease. Recently, a machine learning based expert prediction system and related prediction rules were proposed. However, the success of this system is not satisfactory and should be improved. Furthermore, medical experts find it difficult to interpret the suggested rules of this system. The decision tree-based method was accordingly used in this study to determine the rules of predicting the success of wart treatment methods. According to findings, the success rate varied from 90 to 95% according to the treatment method; these rates are higher than previously reported. Furthermore, the decision tree rules that were determined can be transformed into images to visually interpret the success rates of treatment methods as a function of patient age and the time elapsed since disease appearance. This study provides a method for simple and more accurate interpretation of rules for medical experts. The success of treatment methods is now predictable as a percentage.
  • Design and miniaturization of dual band implantable antennas
    • Abstract: Publication date: Available online 6 July 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Soheil Hashemi, Jalil Rashed-Mohassel Two types of miniaturized dual band implantable antennas are designed and presented, one of a meander type and the other is the so called comb antenna. In medical applications the electromagnetic characteristic changes of tissue in different situations and the corresponding resonant frequency shifts, should not disturb the data transmission. The objective is to design dual band antennas in 400 MHz and 2.4 GHz with suitable bandwidths and small sizes. The meander type antenna was fabricated and its S parameters were measured using an equivalent liquid phantom of skin, fat and muscle which included propanol, butanol, purified water and salt. The experimental results are shown and compared.
  • Continuous blood glucose level prediction of Type 1 Diabetes based on
           Artificial Neural Network
    • Abstract: Publication date: Available online 30 June 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Jaouher Ben Ali, Takoua Hamdi, Nader Fnaiech, Véronique Di Costanzo, Farhat Fnaiech, Jean-Marc Ginoux Recent technological advancements in diabetes technologies, such as Continuous Glucose Monitoring (CGM) systems, provide reliable sources to blood glucose data. Following its development, a new challenging area in the field of artificial intelligence has been opened and an accurate prediction method of blood glucose levels has been targeted by scientific researchers. This article proposes a new method based on Artificial Neural Networks (ANN) for blood glucose level prediction of Type 1 Diabetes (T1D) using only CGM data as inputs. To show the efficiency of our method and to validate our ANN, real CGM data of 13 patients were investigated. The accuracy of the strategy is discussed based on some statistical criteria such as the Root Mean Square Error (RMSE) and the Mean Absolute Percentage Error (MAPE). The obtained averages of RMSE are 6.43 mg/dL, 7.45 mg/dL, 8.13 mg/dL and 9.03 mg/dL for Prediction Horizon (PH) respectively 15 min, 30 min, 45 min and 60 min and the average of MAPE was 3.87% for PH = 15 min, knowing that the smaller is the RMSE and MAPE, the more accurate is the prediction. Experimental results show that the proposed ANN is accurate, adaptive, and very encouraging for a clinical implementation. Furthermore, while other studies have only focused on the prediction accuracy of blood glucose, this work aims to improve the quality of life of T1D patients by using only CGM data as inputs and by avoiding human intervention.
  • Early Detection of Sudden Cardiac Death Using Nonlinear Analysis of Heart
           Rate Variability
    • Abstract: Publication date: Available online 29 June 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Mohammad Khazaei, Khadijeh Raeisi, Ateke Goshvarpour, Maryam Ahmadzadeh Background and ObjectiveSudden cardiac death (SCD) is one of the most widespread reasons for death around the world. A precise and early prediction of SCD can improve the chance of survival by administering cardiopulmonary resuscitation (CPR). Hence, there is a vital need for an SCD prediction system.MethodsIn this work, a novel and efficient algorithm for automated detection of SCD six minutes before its onset is proposed. This algorithm uses features based on the nonlinear modeling of heart rate variability (HRV). In fact, after the extraction of the HRV signals, increment entropy and recurrence quantification analysis-based features are extracted. The one-way ANOVA is applied for the dimension reduction of feature space—this results in lower computational cost. Finally, the distinguishing features are fed to classifiers such as the decision tree, K-nearest neighbor, naive Bayes, and the support vector machine.ResultsBy using the decision tree classifier we have achieved SCD detection six minutes before its onset with an accuracy, specificity, and sensitivity of 95%. These results demonstrate the superiority of the presented algorithm compared to the existing ones in performance.ConclusionsThis study shows that a combination of features based on the nonlinear modeling of HRV, such as laminarity (based on recurrence quantification analysis), and increment entropy leads to early detection of sudden cardiac death. Choosing the decision tree improves the performance of the algorithm. The results could help in the development of a tool that would allow the detection of cardiac arrest six minutes before its onset.
  • Parkinson's disease monitoring from gait analysis via foot-worn sensors
    • Abstract: Publication date: Available online 28 June 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Tunç Aşuroğlu, Koray Açıcı, Cağatay Berke Erdaş, Münire Kılınç Toprak, Hamit Erdem, Hasan Oğul BackgroundIn Parkinson’s disease (PD), neuronal loss in the substantia nigra ultimate in dopaminergic denervation of the stiratum is followed by disarraying of the movements’ preciseness, automatism, and agility. Hence, the seminal sign of PD is a change in motor performance of affected individuals. As PD is a neurodegenerative disease, progression of disability in mobility is an inevitable consequence. Indeed, the major cause of morbidity and mortality among patients with PD is the motor changes restricting their functional independence. Therefore, monitoring the manifestations of the disease is crucial to detect any worsening of symptoms timely, in order to maintain and improve the quality of life of these patients.AimThe changes in motion of patients with PD can be ascertained by the help of wearable sensors attached to the limbs of subjects. Then analysing the recorded data for variation of signals would make it possible to figure an individualized profile of the disease. Advancement of such tools would improve understanding of the disease evolution in the long term and simplify the detection of precipitous changes in gait on a daily basis in the short term. In both cases the apperception of such events would contribute to improve the clinical decision making process with reliable data. To this end, we offer here a computational solution for effective monitoring of PD patients from gait analysis via multiple foot-worn sensors.MethodsWe introduce a supervised model that is fed by ground reaction force (GRF) signals acquired from these gait sensors. We offer a hybrid model, called Locally Weighted Random Forest (LWRF), for regression analysis over the numerical features extracted from input signals to predict the severity of PD symptoms in terms of Universal Parkinson Disease Rating Scale (UPDRS) and Hoehn and Yahr (H&Y) scale. From GRF signals sixteen time-domain features and seven frequency-domain features were extracted and used.Results and conclusionAn experimental analysis conducted on a real data acquired from PD patients and healthy controls has shown that the predictions are highly correlated with the clinical annotations. Proposed approach for severity detection has the best correlation coefficient (CC), mean absolute error (MAE) and root mean squared error (RMSE) values with 0.895, 4.462 and 7.382 respectively in terms of UPDRS. The regression results for H&Y Scale discerns that proposed model outperforms other models with CC, MAE and RMSE with values 0.960, 0.168 and 0.306 respectively. In classification setup, proposed approach achieves higher accuracy in comparison with other studies with accuracy and specificity of 99.0% and 99.5% respectively. Main novelty of this approach is the fact that an exact value of the symptom level can be inferred rather than a categorical result that defines the severity of motor disorders.
  • Glossokinetic potential based tongue–machine ınterface for 1-D
           extraction using neural networks
    • Abstract: Publication date: Available online 28 June 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): Kutlucan Gorur, M. Recep Bozkurt, M. Serdar Bascıl, Feyzullah Temurtas Tongue machine interface (TMI) is a tongue-operated assistive technology enabling people with severe disabilities to control their environments using their tongue motion. In many disorders such as amyotrophic lateral sclerosis or stroke, people can communicate with the external world in a limited degree. However, they may be disabled, while their mind is still intact. Various tongue–machine interface techniques has been developed to support these people by providing additional communication pathway. In this study, we aimed to develop a tongue–machine interface approach by investigating pattern of glossokinetic potential (GKP) signals using neural networks via simple right/left tongue touchings to the buccal walls for 1-D control and communication, named as GKP-based TMI. As can be known in the literature, the tongue is connected to the brain via hypoglossal cranial nerve. Therefore, it generally escapes from the severe damages, in spinal cord injuries and was slowly affected than limbs of persons suffering from many neuromuscular degenerative disorders.In this work, 8 male and 2 female naive healthy subjects, aged 22 to 34 years, participated. Multilayer neural network and probabilistic neural network were employed as classification algorithms with root-mean-square and power spectral density feature extraction operations. Then the greatest success rate achieved was 97.25%.This study may serve disabled people to control assistive devices in natural, unobtrusive, speedy and reliable manner. Moreover, it is expected that GKP-based TMI could be a collaboration channel for traditional electroencephalography (EEG)-based brain computer interfaces which have significant inadequacies arisen from the EEG signals.
  • Use of features from RR-time series and EEG signals for automated
           classification of sleep stages in deep neural network framework
    • Abstract: Publication date: Available online 14 June 2018Source: Biocybernetics and Biomedical EngineeringAuthor(s): R.K. Tripathy, U. Rajendra Acharya Sleep is a physiological activity and human body restores itself from various diseases during sleep. It is necessary to get sufficient amount of sleep to have sound physiological and mental health. Nowadays, due to our present hectic lifestyle, the amount of sound sleep is reduced. It is very difficult to decipher the various stages of sleep manually. Hence, an automated system may be useful to detect the different stages of sleep. This paper presents a novel method for the classification of sleep stages based on RR-time series and electroencephalogram (EEG) signal. The method uses iterative filtering (IF) based multiresolution analysis approach for the decomposition of RR-time series into intrinsic mode functions (IMFs). The delta (δ), theta (θ), alpha (α), beta (β) and gamma (γ) waves are evaluated from EEG signal using band-pass filtering. The recurrence quantification analysis (RQA) and dispersion entropy (DE) based features are evaluated from the IMFs of RR-time series. The dispersion entropy and the variance features are evaluated from the different bands of EEG signal. The RR-time series features and the EEG features coupled with the deep neural network (DNN) are used for the classification of sleep stages. The simulation results demonstrate that our proposed method has achieved an average accuracy of 85.51%, 94.03% and 95.71% for the classification of ‘sleep vs wake’, ‘light sleep vs deep sleep’ and ‘rapid eye movement (REM) vs non-rapid eye movement (NREM)’ sleep stages.
  • Time–frequency analysis in infant cry classification using quadratic
           time frequency distributions
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): J. Saraswathy, M. Hariharan, Wan Khairunizam, J. Sarojini, N. Thiyagar, Y. Sazali, Shafriza Nisha This paper presents a new investigation of time–frequency (t–f) based signal processing approach using quadratic time–frequency distributions (QTFDs) namely spectrogram (SPEC), Wigner–Ville distribution (WVD), Smoothed–Wigner Ville distribution (SWVD), Choi–William distribution (CWD) and modified B-distribution (MBD) for classification of infant cry signals. t–f approaches have proved as an efficient approach for applications involving the non stationary signals. In feature extraction, a cluster of t–f based features were extracted by extending the time-domain and frequency-domain features to the joint t–f domain from the generated t–f representation. Conventional features such as mel-frequency cepstral coefficients (MFCCs) and linear prediction coefficients (LPCs) were also extracted in order to compare the effectiveness of the t–f methods. The efficacy of the extracted feature vectors was validated using probabilistic neural network (PNN) and general regression neural network (GRNN). The proposed methodology was implemented to classify different sets of binary classification problems of infant cry signals from different native. The best empirical result of above 90% was reported and revealed the good potential of t–f methods in the context of infant cry classification.
  • Control of speed and direction of electric wheelchair using seat pressure
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Junichi Hori, Hiroki Ohara, Seiya Inayoshi An electric wheelchair controlled through seat pressure mapping was developed to accomplish hands-free operation. The seat pressure mapping resulting from a change in posture was measured using a pressure sensor array seated on the wheelchair in real time. The movements of the upper body were discriminated using template matching. The speed and direction can be controlled based on the similarities between the measured pressure distribution and five templates of neutral, forward, backward, left, and right movements. The developed interface was built into a commercial electric wheelchair. As the results of an experiment show, the proposed wheelchair can be controlled in any direction and velocity.
  • An improved feature based image fusion technique for enhancement of liver
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Sreeja P., Hariharan S. This paper describes two methods for enhancement of edge and texture of medical images. In the first method optimal kernel size of range filter suitable for enhancement of liver and lesions is deduced. The results have been compared with conventional edge detection algorithms. In the second method the feasibility of feature based pixel wise image fusion for enhancing abdominal images is investigated. Among the different algorithms developed in the medical image fusion pixel level fusion is capable of retaining the maximum relevant information with better implementation and computational efficiency. Conventional image fusion includes multi-modal fusion and multi-resolution fusion. The present work attempts to fuse together, texture enhanced and edge enhanced images of the input image in order to obtain significant enhancement in the output image. The algorithm is tested in low contrast medical images. The result shows an improvement in contrast and sharpness of output image which will provide a basis for a better visual interpretation leading to more accurate diagnosis. Qualitative and quantitative performance evaluation is done by calculating information entropy, MSE, PSNR, SSIM and Tenengrad values.
  • Thermal modelling and screening method for skin pathologies using active
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Maria Strąkowska, Robert Strąkowski, Michał Strzelecki, Gilbert De Mey, Bogusław Więcek This paper presents a novel screening approach of human skin pathologies using Active IR Thermography. The inputs of the proposed algorithm are the values of the physical parameters of the skin. Parameters are estimated based on dynamic thermographic measurements of human skin and the developed thermal model of the tissue. The calculations were based on the inverse thermal modelling. Classification was done using Support Vector Machine, Linear Discriminant Analysis and k-Nearest Neighbours classifiers. As an example, one presented the results of screening for psoriasis.
  • Multi-modal framework for automatic detection of diagnostically important
           regions in nonalcoholic fatty liver ultrasonic images
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): R. Bharath, P. Rajalakshmi, Mohammad Abdul Mateen The severity of fat in ultrasonic liver images is quantified based on characteristics of three regions in the image namely diaphragm, periportal veins and texture of liver parenchyma. The characteristics of these regions vary with the severity of fat in the liver, and is subjected to low signal to noise ratio, low contrast, poorly defined organ boundaries, etc., hence locating these regions in ultrasound images is challenging task for the sonographers. Automated detection of these regions will help the sonographers to do accurate diagnosis in shorter time, and also acts as a fundamental step to develop automated diagnostic algorithms. In this paper, we propose a novel multi-modal framework for detecting diaphragm, periportal veins and texture of liver parenchyma in ultrasonic liver ultrasound images. Since the characteristics of these regions differ from each other, we propose a specific algorithm for detecting each region. Diaphragm and periportal veins are detected with the combination of Viola Jones and GIST descriptor based classifier, while homogeneous texture regions are detected with the combination of histogram features based classifier and connected components algorithm. The proposed algorithm when tested on 180 ultrasound liver images, detected the diaphragm, periportal veins and texture regions with an accuracy of 97%, 91% and 100% respectively.
  • Virus–human protein–protein interaction prediction using Bayesian
           matrix factorization and projection techniques
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Esmaeil Nourani, Farshad Khunjush, F. Erdoğan Sevilgen Pathogens infect host organisms by exploiting host cellular mechanisms and evading host defence mechanisms through molecular pathogen–host interactions (PHIs). Discovering new interactions between pathogen and human proteins is very crucial in understanding the infection mechanisms. By analysing interaction networks, the interactions responsible for infectious diseases can be detected and new drugs disabling these interactions can be delivered. In this paper, we propose a method based on Bayesian matrix factorization for predicting PHIs along with a projection-based technique and combine the results by employing an ensemble method. Furthermore, two features, target similarity and attacker similarity, are utilized for the first time in the literature for PHI prediction. The advantages of the proposed methods are two folds. Firstly, they relieve the need for negative samples which is significant since there is no available dataset providing negative samples for most of the pathogenic systems. Secondly, the experiments demonstrate that the proposed approach outperforms state-of-the-art methods; roughly 20% of top 50 predictions are among recently validated interactions. So, the search space for wet-lab experiments to obtain validated interactions can be considerably narrowed down from a huge number of possible interactions.
  • Automated diagnosis of atrial fibrillation ECG signals using entropy
           features extracted from flexible analytic wavelet transform
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Mohit Kumar, Ram Bilas Pachori, U. Rajendra Acharya Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The electrocardiogram (ECG) signals are widely used to diagnose the AF. Automated diagnosis of AF can aid the clinicians to make a more accurate diagnosis. Hence, in this work, we have proposed a decision support system for AF using a novel nonlinear approach based on flexible analytic wavelet transform (FAWT). First, we have extracted 1000 ECG samples from the long duration ECG signals. Then, log energy entropy (LEE), and permutation entropy (PEn) are computed from the sub-band signals obtained using FAWT. The LEE and PEn features are extracted from different frequency bands of FAWT. We have found that LEE features showed better classification results as compared to PEn. The LEE features obtained maximum accuracy, sensitivity, and specificity of 96.84%, 95.8%, and 97.6% respectively with random forest (RF) classifier. Our system can be deployed in hospitals to assist cardiac physicians in their diagnosis.
  • A robust pre-processing of BeadChip microarray images
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Jan Kalina Microarray images commonly used in gene expression studies are heavily contaminated by noise and/or outlying values (outliers). Unfortunately, standard methodology for the analysis of Illumina BeadChip microarray images turns out to be too vulnerable to data contamination by outliers. In this paper, an alternative approach to low-level pre-processing of images obtained by the BeadChip microarray technology is proposed. The novel approach robustifies the standard methodology in a complex way and thus ensures a sufficient robustness (resistance) to outliers. A gene expression data set from a cardiovascular genetic study is analyzed and the performance of the novel robust approach is compared with the standard methodology. The robust approach is able to detect and delete a larger percentage of outliers. More importantly, gene expressions are estimated more precisely. As a consequence, also the performance of a subsequently performed classification task to two groups (patients vs. control persons) is improved over the cardiovascular gene expression data set. A further improvement was obtained when considering weighted gene expression values, where the weights correspond to a robust estimate of variability of the measurements for each individual gene transcript.
  • Geometric verification of the validity of Finite Element Method analysis
           of Abdominal Aortic Aneurysms based on Magnetic Resonance Imaging
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Zuzanna Domagała, Hubert Stępak, Paweł Drapikowski, Anna Kociemba, Małgorzata Pyda, Katarzyna Karmelita-Katulska, Łukasz Dzieciuchowicz, Grzegorz Oszkinis The currently used criterion of maximum transverse diameter for the Abdominal Aortic Aneurysm treatment has some limitations. Attempts to create individualized, therapeutic strategies are being conducted, including biomechanical assessment of rupture risk of an aneurysm based on the Finite Element Analysis of the geometric models.The usual approach is to use the results of the computed tomography imaging to build a three-dimensional model of the aneurysm. The FEA is then performed and the resulting stress is analysed to estimate the risk of rupture. Although such an approach brings significant improvements over the traditional maximum diameter method, it is difficult to ensure the validity of the assumptions made.This paper presents a method to evaluate the correctness of such an approach. The emergence of gated Magnetic Resonance Imaging allows registering aneurysm in both the systolic and diastolic phase of cardiac cycle. The corresponding geometric models are built and the results of the FEA applied to the diastolic model are compared with the actual deformation of the aneurysm observed in the patient's body. Thus, it is possible to verify whether the individualized diagnostic approach applied to a specific patient was correct.The geometry of the reference and the analysed models were compared using the Differential Surface Area Method. The average geometry error equals 1.65%. In the best case the error amounts to 1.04%, in the worst to 3.00%.The obtained results provide evidence that the Finite Element Analysis is a reliable method and can be potentially used for individualized diagnostics and treatment.
  • Experimental investigation of particle size distribution and morphology of
           alumina-yttria-ceria-zirconia powders obtained via sol–gel route
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Damian S. Nakonieczny, Magdalena Antonowicz, Zbigniew K. Paszenda, Tomasz Radko, Sabina Drewniak, Wojciech Bogacz, Cezary Krawczyk BackgroundOxide-doped zirconia is currently commonly used ceramics in dental prosthetics. However, its use raises a lot of controversy. This is related to the stability of the zirconia metastable phases in the human mouth environment and it sensitivity for the so-called low-temperature degradation. A key way to avoid this type of negative phenomena is doping ZrO2 with selected metal oxides and choosing appropriate methods for the synthesis of ceramic powders.ObjectiveThe aim of this paper is to present investigations of modification and to analyse the influence of chemical composition and volume of parent-solvent for the morphology and thermal properties of ceramic powders prepared in a ZrO2-CeO2-Y2O3-Al2O3 system.MethodsThe powders were obtained by using the sol–gel method in an inert gas atmosphere and ambient temperature using zirconium n-propoxide for this purpose. Morphology was examined by using scanning electron microscopy (SEM) and particle size distribution (PSD); thermal properties was evaluated using thermogravimetric analysis (TGA/DTA/DTG), and chemical composition was confirmed by using electron probe microanalysis (EPMA)ResultsDepending from the volume of the CeO2 precursor solution of and regardless of the volume of the second oxide precursor, was observed difference in morphology of the obtained powders. Overall trend is related to reduce the size of agglomerates with an increase in the volume of the precursor of CeO2.ConclusionsThe influence of various chemical compositions for morphology and thermal properties is negligible. In contrast, a clear correlation is observed between the volume of parent alcohol for both morphology and thermal properties. Use of sol–gel method to further research in view of these results appears to be appropriate.
  • Generalized Stockwell transform and SVD-based epileptic seizure detection
           in EEG using random forest
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Tao Zhang, Wanzhong Chen, Mingyang Li PurposeVisual inspection of electroencephalogram (EEG) records by neurologist is the main diagnostic method of epilepsy but it is particularly time-consuming and expensive. Hence, it is of great significance to develop automatic seizure detection technique.MethodsIn this work, a seizure detection approach, synthesizing generalized Stockwell transform (GST), singular value decomposition (SVD) and random forest, was proposed. Utilizing GST, the raw EEG was transformed into a time–frequency matrix, then the global and local singular values were extracted by SVD from the holistic and partitioned matrices of GST, respectively. Subsequently, four local parameters were calculated from each vector of local singular values. Finally, the global singular value vectors and local parameters were respectively fed into two random forest classifiers for classification, and the final category of a testing EEG was voted based on sub-labels obtained from the trained classifiers.ResultsFour most common but challenging classification tasks of Bonn EEG database were investigated. The highest accuracies of 99.12%, 99.63%, 99.03% and 98.62% were achieved using our presented technique, respectively.ConclusionsOur proposed technique is comparable or superior to other up-to-date methods. The presented method is promising and able to handle with kinds of epileptic seizure detection tasks with satisfactory accuracy.
  • A hybrid approach for the delineation of brain lesion from CT images
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Anjali Gautam, Balasubramanian Raman, Shailendra Raghuvanshi Brain lesion segmentation from radiological images is the most important task in accurate diagnosis of patients. This paper presents a hybrid approach for the segmentation of brain lesion from computed tomography (CT) images based on the combination of fuzzy clustering using hyper tangent function as the robust kernel and distance regularized level set evolution (DRLSE) function as the edge based active contour method. Kernel based fuzzy clustering method divides the image into different regions. These regions can be used to find region of interest by using DRLSE algorithm to generate the optimal region boundary. The proposed method results in smooth boundary of the required regions with high accuracy of segmentation. In this paper, results are compared with standard fuzzy c-means (FCM) clustering, spatial FCM, robust kernel based fuzzy clustering (RFCM) and DRLSE algorithms. The performance of the proposed method is evaluated on CT scan images of hemorrhagic lesion, which shows that our method can segment brain lesion more accurately than the other conventional methods.
  • Temperature controlled dual hypoxic chamber design for in vitro
           ischemia experiments
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Marcell Bagó, Dénes B. Horváthy, Melinda Simon, Bence Marschall, Ana Pinto, Olga Kuten, Dora Polsek, István Hornyák, Stefan Nehrer, Zsombor Lacza In vitro ischemia models are designed to study various aspects of hypo-perfusion, focusing on the consequences of acute events under body temperature. Cold ischemia is less investigated even though the beneficial effects of cooling is expected. The aim of the present work was to develop a device modeling cold and warm ischemia in vitro. Oxygen-glucose deprivation was applied with continuous nitrogen flow and glucose-free cell culture media to mimic ischemia. The temperature in both chambers were independently set between 4 and 37 °C. Samples were placed inside for the ischemic period, followed by a reperfusion stage under standard cell culture conditions. We tested rat calvaria bone pieces undergoing 1, 7, 12 and 24 h of ischemia at 4 and 37 °C. After 24 h of reperfusion, cell number was measured with a tetrazolium cell viability assay. One hour of warm ischemia paradoxically increased the post-reperfusion cell count, while cold-ischemia had an opposite effect. After 7 h of warm ischemia the cells were already unable to recover, while under cold ischemia 60% of the cells were still functioning. After 12 h of cold ischemia 50% of the cells were still be able to recover, while at 24 h even the low temperature was unable to keep the cells alive. The markedly different effect of warm and cold ischemia suggests that this newly designed system is capable of reliable and reproducible modeling of ischemic conditions. Moreover, it also enables deeper investigations in the pathophysiology of cold ischemia at cellular and tissue level.
  • Improvement in the diagnosis of melanoma and dysplastic lesions by
           introducing ABCD-PDT features and a hybrid classifier
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Arezoo Zakeri, Alireza Hokmabadi Melanoma and dysplastic lesions are pigmented skin lesions whose accurate classification is of great importance. In this paper, we have proposed a computer-aided diagnosis (CAD) system to improve the diagnostic ability of the conventional ABCD (asymmetry, border irregularity, color, and diameter) analysis. We introduced features extracted by local analysis of range of intensity variations within the lesion that describe pigment distribution and texture (PDT) features. The statistical distribution of pigmentation at a specified direction and distance was analyzed through grey level co-occurrence matrix (GLCM). Some other quantitative features were also extracted by computing neighborhood grey-tone difference matrix. These were correlated with human perception of texture. A hybrid classifier was designed for classification of melanoma, dysplastic, and benign lesions. Log-linearized Gaussian mixture neural network (LLGMNN), K-nearest neighborhood (KNN), linear discriminant analysis (LDA), and support vector machine (SVM) construct the hybrid classifier. The proposed system was evaluated on a set of 792 dermoscopy images and the diagnostic accuracies of 96.8%, 97.3%, and 98.8% for melanoma, dysplastic, and benign lesions were achieved, respectively. The results indicate that PDT features are promising features which in combination with the conventional ABCD features are capable of enhancing the classification performance of the pigmented skin lesions.
  • Automatic identifying of maternal ECG source when applying ICA in fetal
           ECG extraction
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Qiong Yu, Huawen Yan, Lin Song, Wenya Guo, Hongxing Liu, Junfeng Si, Ying Zhao Independent component analysis (ICA) is usually used as a preliminary step for maternal electrocardiogram (ECG) QRS detection in fetal ECG extraction. When applying ICA to do this, a troublesome problem arises from how to automatically identify the separated maternal ECG component. In this paper we proposed a method called PRCH (short for Peak to peak entropy, R-R interval entropy, Correlation coefficient and Heart rate) for the automatic identifying. In the method, we defined four kinds of features, including amplitude, instantaneous heart rate, morphology and average heart rate, to characterize a signal, and determined some decision parameters through machine learning. Experiments and comparison with other three existed methods were given. Through taking metric F1 for evaluation, it showed that the proposed PRCH method has the highest identifying accuracy and generalization capability.
  • A fast and robust level set motion-assisted deformable registration method
           for volumetric CT guided lung intervention
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Daegwan Kim, Namkug Kim, Sangmin Lee, Joon Beom Seo This paper describes the accurate deformable registration method for image-guided lung interventions, including lung nodule biopsy and radiofrequency ablation of lung tumours. A level set motion assisted deformable registration method for computed tomography (CT) images was proposed and its accuracy and speed were compared with those of other conventional methods. Fifteen 3D CT images obtained from lung biopsy patients were scanned. Each scan consisted of diagnostic and preoperative CT images. Each deformable registration method was initially evaluated with a landmark-based affine registration algorithm. Various deformable registration methods such as level set motion, demons, diffeomorphic demons, and b-spline were compared. Visual assessment by two expert thoracic radiologists using five scales showed an average visual score of 3.2 for level set motion deformable registration, whereas scores were below 3 for other deformable registration methods. In the qualitative assessment, the level set motion algorithm showed better results than those obtained with other deformable registration methods. A level set motion based deformable registration algorithm was effective for registering diagnostic and preoperative volumetric CT images for image-guided lung intervention.
  • Enhancement of graphene quantum dots based applications via optimum
           physical chemistry: A review
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Pushpa Jegannathan, Amin Termeh Yousefi, Mohd Sayuti Abd Karim, Nahrizul Adib Kadri Graphene quantum dots (GQDs) is a promising new substance from the carbon material family that has been attracting researchers of many fields, such as biomedical sensors, medical imaging, polymer science, solar cells, light emitting diodes, and photoelectrons. Its unique electrical and mechanical properties could encourage its usage due to its low cost, high surface area, safety, stable luminescence, excellent biocompatibility, suitable conductivity, and low toxicity. The dispersibility of GQDs in common solvents depends on hydrophobicity/hydrophilicity, which is particularly important toward its homogeneous incorporation into various polymer layers. This review discusses the global demand for GQDs and explore the main factors encouraging its utilization in various devices. Moreover, different synthesis methods of GQDs were compared, and recent investigation on GQDs based composite applications are analyzed. Finally, the future of GQDs is detailed, focusing on the gaps in its role in future technology.
  • Peripheral blood smear analysis using image processing approach for
           diagnostic purposes: A review
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Roopa B. Hegde, Keerthana Prasad, Harishchandra Hebbar, I Sandhya Peripheral blood smear analysis is a common practice to evaluate health status of a person. Many disorders such as malaria, anemia, leukemia, thrombocytopenia, sickle cell anemia etc., can be diagnosed by evaluating blood cells. Many groups have reported methods to automate blood smear analysis for detection of specific disorders for diagnostic purposes. In this paper, we have summarized the methods used to analyze peripheral blood smears using image processing techniques. We have categorized these methods into three groups based on approaches such as WBC analysis, RBC analysis and platelet analysis. We conclude that there is a need for a method of automation to match with human evaluation process and rule out any abnormality present in the blood smear. It is desirable for studies on automation of peripheral blood smear analysis to focus on development of robust method to handle illumination and color shade variations. Also, it is desirable to design a method which could collect all the abnormal regions from all views of a specimen to limit the manual evaluation to those regions making it more feasible for telemedicine applications.
  • EEG with a reduced number of electrodes: Where to detect and how to
           improve visually, auditory and somatosensory evoked potentials
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Sebastian A.F. Stehlin, Xuan P. Nguyen, Markolf H. Niemz The measurement of evoked potentials has become a standard tool to test new hardware and software for electroencephalography (EEG). In this study, we investigate where to detect and how to improve visually, auditory and somatosensory evoked potentials with a reduced number of electrodes. We measured a total of 50 evoked potentials in healthy subjects, and we were able to detect visually, auditory and somatosensory evoked potentials with just three electrodes. We also investigated where to measure a combination of visually, auditory and somatosensory evoked potentials and found the best positions to be Oz, O1, O2, TP9 and TP10. In the second part of this study, we analyzed how the evoked potentials depend on the segmentation frequency selected to superpose EEG responses. We found that the detection of visually evoked potentials requires the segmentation frequency to match the stimulus frequency with an accuracy of at least 99.92 percent. The detection of auditory evoked potentials and somatosensory evoked potentials requires a matching of at least 99.95 percent. Therefore, a correct matching of the segmentation frequency with the stimulation frequency is the primary key to improving the quality of evoked potentials.
  • Estimation of severity level of non-proliferative diabetic retinopathy for
           clinical aid
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Jaskirat Kaur, Deepti Mittal Diabetic retinopathy, a symptomless complication of diabetes, is one of the significant causes of vision impairment in the world. The early detection and diagnosis can reduce the occurrence of severe vision loss due to diabetic retinopathy. The diagnosis of diabetic retinopathy depends on the reliable detection and classification of bright and dark lesions present in retinal fundus images. Therefore, in this work, reliable segmentation of lesions has been performed using iterative clustering irrespective of associated heterogeneity, bright and faint edges. Afterwards, a computer-aided severity level detection method is proposed to aid ophthalmologists for appropriate treatment and effective planning in the diagnosis of non-proliferative diabetic retinopathy. This work has been performed on a composite database of 5048 retinal fundus images having varying attributes such as position, dimensions, shapes and color to make a reasonable comparison with state-of-the-art methods and to establish generalization capability of the proposed method. Experimental results on per-lesion basis show that the proposed method outperforms state-of-the-methods with an average sensitivity/specificity/accuracy of 96.41/96.57/94.96 and 95.19/96.24/96.50 for bright and dark lesions respectively on composite database. Individual per-image based class accuracies delivered by the proposed method: No DR-95.9%, MA-98.3%, HEM-98.4%, EXU-97.4% and CWS-97.9% demonstrate the clinical competence of the method. Major contribution of the proposed method is that it efficiently grades the severity level of diabetic retinopathy in spite of huge variations in retinal images of different databases. Additionally, the substantial combined performance of these experiments on clinical and open source benchmark databases support a strong candidature of the proposed method in the diagnosis of non-proliferative diabetic retinopathy.
  • Automatic detection of tuberculosis bacilli from microscopic sputum smear
           images using deep learning methods
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Rani Oomman Panicker, Kaushik S. Kalmady, Jeny Rajan, M.K. Sabu An automatic method for the detection of Tuberculosis (TB) bacilli from microscopic sputum smear images is presented in this paper. According to WHO, TB is the ninth leading cause of death all over the world. There are various techniques to diagnose TB, of which conventional microscopic sputum smear examination is considered to be the gold standard. However, the aforementioned method of diagnosis is time intensive and error prone, even in experienced hands. The proposed method performs detection of TB, by image binarization and subsequent classification of detected regions using a convolutional neural network. We have evaluated our algorithm using a dataset of 22 sputum smear microscopic images with different backgrounds (high density and low-density images). Experimental results show that the proposed algorithm achieves 97.13% recall, 78.4% precision and 86.76% F-score for the TB detection. The proposed method automatically detects whether the sputum smear images is infected with TB or not. This method will aid clinicians to predict the disease accurately in a short span of time, thereby helping in improving the clinical outcome.
  • Discriminant analysis of neural style representations for breast lesion
           classification in ultrasound
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Michał Byra Ultrasound imaging is widely used for breast lesion differentiation. In this paper we propose a neural transfer learning method for breast lesion classification in ultrasound. As reported in several papers, the content and the style of a particular image can be separated with a convolutional neural network. The style, coded by the Gram matrix, can be used to perform neural transfer of artistic style. In this paper we extract the neural style representations of malignant and benign breast lesions using the VGG19 neural network. Next, the Fisher discriminant analysis is used to separate those neural style representations and perform classification. The proposed approach achieves good classification performance (AUC of 0.847). Our method is compared with another transfer learning technique based on extracting pooling layer features (AUC of 0.826). Moreover, we apply the Fisher discriminant analysis to differentiate breast lesions using ultrasound images (AUC of 0.758). Additionally, we extract the eigenimages related to malignant and benign breast lesions and show that these eigenimages present features commonly associated with lesion type, such as contour attributes or shadowing. The proposed techniques may be useful for the researchers interested in ultrasound breast lesion characterization.
  • Representation learning-based unsupervised domain adaptation for
           classification of breast cancer histopathology images
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Pendar Alirezazadeh, Behzad Hejrati, Alireza Monsef-Esfahani, Abdolhossein Fathi Breast cancer has high incidence rate compared to the other cancers among women. This disease leads to die if it does not diagnosis early. Fortunately, by means of modern imaging procedure such as MRI, mammography, thermography, etc., and computer systems, it is possible to diagnose all kind of breast cancers in a short time. One type of BC images is histology images. They are obtained from the entire cut-off texture by use of digital cameras and contain invaluable information to diagnose malignant and benign lesions. Recently by requesting to use the digital workflow in surgical pathology, the diagnosis based on whole slide microscopy image analysis has attracted the attention of many researchers in medical image processing. Computer aided diagnosis (CAD) systems are developed to help pathologist make a better decision. There are some weaknesses in histology images based CAD systems in compared with radiology images based CAD systems. As these images are collected in different laboratory stages and from different samples, they have different distributions leading to mismatch of training (source) domain and test (target) domain. On the other hand, there is the great similarity between images of benign tumors with those of malignant. So if these images are analyzed undiscriminating, this leads to decrease classifier performance and recognition rate. In this research, a new representation learning-based unsupervised domain adaptation method is proposed to overcome these problems. This method attempts to distinguish benign extracted feature vectors from those of malignant ones by learning a domain invariant space as much as possible. This method achieved the average classification rate of 88.5% on BreaKHis dataset and increased 5.1% classification rate compared with basic methods and 1.25% with state-of-art methods.
  • Object detection based on deep learning for urine sediment examination
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): Yixiong Liang, Zhihong Tang, Meng Yan, Jianfeng Liu Urine sediment examination (USE) is an important topic in kidney disease analysis and it is often the prerequisite for subsequent diagnostic procedures. We propose DFPN(Feature Pyramid Network with DenseNet) method to overcome the problem of class confusion in the USE images that it is hard to be solved by baseline model which is the state-of-the-art object detection model FPN with RoIAlign pooling. We explored the importance of two parts of baseline model for the USE cell detection. First, adding attention module in the network head, and the class-specific attention module has improved mAP by 0.7 points with pre-trained ImageNet model and 1.4 points with pre-trained COCO model. Next, we introduced DenseNet to the baseline model(DFPN) for cell detection in USE, so that the input of the network's head own multiple levels of semantic information, compared to the baseline model only has high-level semantic information. DFPN achieves top result with a mAP of 86.9% on USE test set after balancing between the classification loss and bounding-box regression loss, which improve 5.6 points compared to baseline model, and especially erythrocyte's AP is greatly improved from 65.4% to 93.8%, indicating class confusion has been basically resolved. And we also explore the impacts of training schedule and pre-trained model. Our method is promising for the development of automated USE.
  • Bayesian HCS-based multi-SVNN: A classification approach for brain tumor
           segmentation and classification using Bayesian fuzzy clustering
    • Abstract: Publication date: 2018Source: Biocybernetics and Biomedical Engineering, Volume 38, Issue 3Author(s): A. Ratna Raju, P. Suresh, R. Rajeswara Rao Brain tumor segmentation and classification is the interesting area for differentiating the tumerous and the non-tumerous cells in the brain and to classify the tumerous cells for identifying its level. The conventional methods lack the automatic classification and they consumed huge time and are ineffective in decision-making. To overcome the challenges faced by the conventional methods, this paper proposes the automatic method of classification using the Harmony-Crow Search (HCS) Optimization algorithm to train the multi-SVNN classifier. The brain tumor segmentation is performed using the Bayesian fuzzy clustering approach, whereas the tumor classification is done using the proposed HCS Optimization algorithm-based multi-SVNN classifier. The proposed method of classification determines the level of the brain tumor using the features of the segments generated based on Bayesian fuzzy clustering. The robust features are obtained using the information theoretic measures, scattering transform, and wavelet transform. The experimentation performed using the BRATS database conveys proves the effectiveness of the proposed method and the proposed HCS-based tumor segmentation and classification achieves the classification accuracy of 0.93 and outperforms the existing segmentation methods.
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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