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Journal Cover Biocybernetics and Biological Engineering
  [SJR: 0.279]   [H-I: 8]   [4 followers]  Follow
    
   Full-text available via subscription Subscription journal  (Not entitled to full-text)
   ISSN (Print) 0208-5216
   Published by Elsevier Homepage  [3039 journals]
  • Full-automatic computer aided system for stem cell clustering using
           Content-based Microscopic Image Analysis
    • Authors: Chen Li; Xinyu Huang; Tao Jiang; Ning Xu
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Chen Li, Xinyu Huang, Tao Jiang, Ning Xu
      Stem cells are very original cells that can differentiate into other cells, tissues and organs, which play a very important role in biomedical treatments. Because of the importance of stem cells, in this paper we propose a full-automatic computer aided clustering system to assist scientists to explore potential co-occurrence relations between the cell differentiation and their morphological information in phenotype. In this proposed system, a multi-stage Content-based Microscopic Image Analysis (CBMIA) framework is applied, including image segmentation, feature extraction, feature selection, feature fusion and clustering techniques. First, an Improved Supervised Normalized Cuts (ISNC) segmentation algorithm is newly introduced to partition multiple stem cells into individual regions in an original microscopic image, which is the most important contribution in this paper. Then, based on the segmented stem cells, 11 different feature extraction approaches are applied to represent the morphological characteristics of them. Thirdly, by analysing the robustness and stability of the extracted features, Hu and Zernike moments are selected. Fourthly, these two selected features are combined by an early fusion approach to further enhance the properties of the feature representation of stem cells. Finally, k-means clustering algorithm is chosen to classify stem cells into different categories using the fused feature. Furthermore, in order to prove the effectiveness and usefulness of this proposed system, we carry out a series of experiments to evaluate our methods. Especially, our ISNC segmentation obtains 92.4% similarity, 96.0% specificity and 107.8% ration of accuracy, showing the potential of our work.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.01.004
       
  • Non-uniform viscosity caused by red blood cell aggregation may affect NO
           concentration in the microvasculature
    • Authors: Huiting Qiao; Hongjun Zhao; Dov Jaron
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Huiting Qiao, Hongjun Zhao, Dov Jaron
      Aggregation of red blood cells in the micro vasculature may affect blood viscosity in the vessel. The purpose of this study was to investigate the potential effect of non-uniform viscosity caused by red blood cell (RBC) aggregation on nitric oxide (NO) concentration and distribution. A 3-D multi-physics model was established to simulate the production, transport and consumption of NO. Two non-uniform viscosity models caused by RBC aggregation were investigated: one assuming a linear and the other a step hematocrit distribution. In addition, the effect of the thickness of the plasma layer was tested. Simulation results demonstrate that non-uniform viscosity caused by RBCs aggregation influences NO concentration distribution. Compared with the uniform viscosity model, NO concentration using non-uniform viscosity is lower than that using uniform viscosity. Moreover, NO concentration calculated from the step hematocrit model is higher than that calculated from the linear hematocrit model. NO concentrations in the endothelium and the vascular wall decrease with the decline of the thickness of the plasma layer. The relative decrease differs between the linear and the step model. Our results suggest that non-uniform viscosity caused by red blood cell aggregation affects nitric oxide distribution in the micro vasculature. If uniform viscosity is assumed when performing numerical simulations, NO concentration values may be overestimated.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2016.10.004
       
  • An efficient wavelet-based automated R-peaks detection method using
           Hilbert transform
    • Authors: Manas Rakshit; Susmita Das
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Manas Rakshit, Susmita Das
      Machine-aided detection of R-peaks is becoming a vital task to automate the diagnosis of critical cardiovascular ailments. R-peaks in Electrocardiogram (ECG) is one of the key segments for diagnosis of the cardiac disorder. By recognizing R-peaks, heart rate of the patient can be computed and from that point onwards heart rate variability (HRV), tachycardia, and bradycardia can also be determined. Most of the R-peaks detectors suffer due to non-stationary behaviors of the ECG signal. In this work, a wavelet transform based automated R-peaks detection method has been proposed. A wavelet-based multiresolution approach along with Shannon energy envelope estimator is utilized to eliminate the noises in ECG signal and enhance the QRS complexes. Then a Hilbert transform based peak finding logic is used to detect the R-peaks without employing any amplitude threshold. The efficiency of the proposed work is validated using all the ECG signals of MIT-BIH arrhythmia database, and it attains an average accuracy of 99.83%, sensitivity of 99.93%, positive predictivity of 99.91%, error rate of 0.17% and an average F-score of 0.9992. A close observation of the simulation and validation indicates that the suggested technique achieves superior performance indices compared to the existing methods for real ECG signal.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.02.002
       
  • Investigation of opacity development in the human eye for estimation of
           the postmortem interval
    • Authors: İsmail Cantürk; Safa Çelik; M. Feyzi Şahin; Fatih Yağmur; Sadık Kara; Fethullah Karabiber
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): İsmail Cantürk, Safa Çelik, M. Feyzi Şahin, Fatih Yağmur, Sadık Kara, Fethullah Karabiber
      Estimation of the postmortem interval (PMI) has attracted the attention of many researchers. It is generally accepted as a challenging task in forensic medicine. Due to its difficulty, researchers have tried to estimate the PMI using different physical and chemical techniques. Since the PMI estimation accuracies of previous studies are not at the desired level, new methods should be developed to more accurately estimate the PMI. The development of opacity in the eye in the PMI might be an important breakthrough in this field. After death, corneal hydration occurs due to degenerated endothelial cells. The degenerated endothelial barrier of the cornea cannot prevent the flow of aqueous humor to the cornea, which results in opacity. The amount of aqueous humor in the cornea determines the level of opacity. Since the flow of aqueous humor to the cornea will continue for a while, opacity is expected to increase with the PMI. In this study, images of human eyes were investigated using computer-based image analysis. The corneal and non-corneal opacities of the recorded eye images increase during the experiment. The experimental results prove that there is a correlation between the elapsed time after death and the development of opacity in the corneal and non-corneal regions in human cases. Exponential curve fitting is employed to observe the decay of the opacity over time. A repeated ANOVA test is also used to show that the opacity development is statistically significant.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.02.001
       
  • A hierarchical classification method for automatic sleep scoring using
           multiscale entropy features and proportion information of sleep
           architecture
    • Authors: Pan Tian; Jie Hu; Jin Qi; Xian Ye; Datian Che; Ying Ding; Yinghong Peng
      Abstract: Publication date: Available online 16 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Pan Tian, Jie Hu, Jin Qi, Xian Ye, Datian Che, Ying Ding, Yinghong Peng
      Background Sleep scoring is a critical step in medical researches and clinical applications. Traditional visual scoring method is based on the processing of physiological signals, such as electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG), which is a time consuming and subjective procedure. It is an urgent task to develop an effective method for automatic sleep scoring. Method This paper presents a hierarchical classification method for automatic sleep scoring by combining multiscale entropy features with the proportion information of the sleep architecture. Based on a three-layer classification scheme, sleep is categorized into five stages (Awake, S1, S2, SWS and REM). Specifically, the first layer is a standard SVM which performs classification between Awake and Sleep, while the second and third layers are implemented by combining probabilistic output SVM with proportion-based clustering. Multiscale entropy (MSE) from electroencephalogram (EEG) is extracted to represent signal characteristics in multiple temporal scales. Results The proposed method is evaluated with 20 sleep recordings, including 10 subjects with mild difficulty falling asleep and 10 healthy subjects. The overall accuracy of the proposed method is 91.4%. Compared with traditional methods, the classification accuracy of the proposed method is more balanced and the global performance is much better. The dataset includes both healthy subjects and subjects with sleep disorders, which means the presented method has good generalization capacity. Experimental results demonstrate the feasibility of the attempt to introduce proportion information into automatic sleep scoring.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.01.005
       
  • Stress–strain characteristic of human trabecular bone based on depth
           sensing indentation measurements
    • Authors: Marek Pawlikowski; Konstanty Skalski; Jakub Bańczerowski; Anna Makuch; Krzysztof Jankowski
      Abstract: Publication date: Available online 15 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Marek Pawlikowski, Konstanty Skalski, Jakub Bańczerowski, Anna Makuch, Krzysztof Jankowski
      In the paper a relation between stress and strain for trabecular bone is presented. The relation is based on the results of depth sensing indentation (DSI) tests which were performed with a spherical indenter. The DSI technique allowed also to determine three measures of hardness, i.e. Martens hardness (H M), nanohardness (H IT), Vickers hardness (H V) and Young modulus E IT of the trabecular bone tissue. The bone samples were harvested from human femoral heads during orthopaedical procedures of hip joint implantation. In the research the Hertzian approach is undertaken. The constitutive relation is then formulated in the elastic domain. The values of hardness and the Young modulus obtained from the DSI tests are in good agreement with those found in literature. The stress–strain relation is formulated to implement it in the future in finite element analyses of trabecular bone. Such simulations allow to take into account the microstructural mechanical properties of the trabecular tissue as well as remodelling phenomenon. This will make it possible to analyse the stress and strain states in bone for engineering and medical purposes.

      PubDate: 2017-02-20T02:35:05Z
      DOI: 10.1016/j.bbe.2017.01.002
       
  • Stress response of patellofemoral joint subjected to femoral retroversion
           with various patellar kinematics and flexions – An FEA study
    • Authors: Marlon Jones Louis; R. Malayalamurthi
      Abstract: Publication date: Available online 10 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Marlon Jones Louis, R. Malayalamurthi
      The purpose of this study is to observe the stress response of the patellofemoral joint associated with three patellar kinematics: shift, spin and tilt under femoral retroversion conditions. By assigning various flexions and different loads, the stresses were quantified in the bones, tendons, cartilages and cartilage–bone interface. Four different loads of 600, 657, 706 and 753N were applied on 12 models representing each of the various kinematics of shifts, spins and tilts of the patella with femoral flexions of 30°, 60°, 90° and 120° which gave results for 48 analyses. The ‘Q’ angle of the femur bone was maintained at 14° with femoral retroversion of 21°. Based on the patellar kinematics, three different cases were modeled as (a) 5mm shift 10° spin 4° tilt, (b) 10mm shift 13° spin 8° tilt, and (c) 15mm shift 16° spin 12° tilt. Medial shift, spin and tilt with femoral retroversion were limited in this study. The femoral displacement for 30° flexion at 600N was found to be same in all the (a), (b), and (c) cases. Similarly, respective same displacements were achieved in all three cases when subjected to 60° flex at 657N, 90° flex at 706N and 120° flex at 753N. From the simulated results it is inferred that femoral retroversion with case (b) kinematics susceptibly dominated by the cartilages causes patellofemoral joint pain, arthritis and instability due to the larger contact areas between the patella and femur bone at flexions 60° and 90°.

      PubDate: 2017-02-13T23:08:38Z
      DOI: 10.1016/j.bbe.2016.12.006
       
  • Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy
           regression model
    • Authors: Narges Shafaei Bajestani; Ali Vahidian Kamyad; Ensieh Nasli Esfahani; Assef Zare
      Abstract: Publication date: Available online 5 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Narges Shafaei Bajestani, Ali Vahidian Kamyad, Ensieh Nasli Esfahani, Assef Zare
      Choosing a proper method to predict and timely prevent the complications of diabetes could be considered a significant step toward optimally controlling the disease. Since in medical research only small sample sizes of data are available and medical data always includes high levels of uncertainty and ambiguity, a type-2 fuzzy regression model seems to be an appropriate procedure for finding the relationship between outcome and explanatory variables in medical decision-making. In this paper, a new type-2 fuzzy regression model based on type-2 fuzzy time series concepts is used to forecast nephropathy in diabetic patients. Results in two examples show model efficiency. The use of such models in diabetes clinics is proposed.

      PubDate: 2017-02-08T20:51:04Z
      DOI: 10.1016/j.bbe.2017.01.003
       
  • Finite element analysis of stresses generated in cortical bone during
           implantation of a novel Limb Prosthesis Osseointegrated Fixation System
    • Authors: Piotr Prochor
      Abstract: Publication date: Available online 4 February 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Piotr Prochor
      The aim of this study was a biomechanical evaluation of the stresses generated in bone during implantation of the implant designed for direct skeletal attachment of limb prosthesis and a typical interference-fit implant of the reference. Using the finite element method implantation processes of both implants were modelled. The influence of two factors on stresses generated in bone was analysed: first – the radial interference between the implant and reamed marrow cavity (0.05mm up to 0.25mm) and second – the three types of implant's surfaces: polished, beaded and flaked. Obtained results show that in the case of the smallest value of radial interference (0.05mm), stresses generated in cortical bone are more appropriate for the reference implant than for the designed one. With the increase of both analysed factors generated stresses are in favour of the designed implant especially in longitudinal direction for both, implant-adjacent and deep cortical tissue (even 18 times lower) alike. Stresses patterns also present that stresses values are lower in overall volume of analysed bone's part, during implantation of the designed implant. Presented characteristics and patterns confirm that the implantation method of presented implant is safer than a method for typical interference-fit implants for direct skeletal attachment of limb prosthesis.

      PubDate: 2017-02-08T20:51:04Z
      DOI: 10.1016/j.bbe.2016.12.001
       
  • A generalized method for the detection of vascular structure in
           pathological retinal images
    • Authors: Jaskirat Kaur; Deepti Mittal
      Abstract: Publication date: Available online 28 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Jaskirat Kaur, Deepti Mittal
      Variations in blood vasculature morphology of retinal fundus images is one of the dominant characteristic for the early detection and analysis of retinal abnormalities. Therefore the accurate interpretation of blood vasculature is useful for ophthalmologists to diagnose patients that suffer from retinal abnormalities. A generalized method to detect and segment blood vasculature using retinal fundus images has been proposed in this work using (i) preprocessing for quality improvement of retinal fundus images, (ii) initial segmentation of vasculature map to find vascular and non vascular structures, (iii) extraction of relevant set of geometrical based features from the vasculature map and intensity based features from original retinal fundus image that differentiate vascular and non vascular structures efficiently, (iv) supervised classification of vascular and non vascular structures using the extracted features, and (v) joining of candidate vascular structures to create connectivity. The proposed method is evaluated on clinically acquired dataset and different publically available standard datasets such as DRIVE, STARE, ARIA and HRF. The clinically acquired dataset consists of 468 retinal fundus images comprising of healthy images, images with mild, intermediate and severe pathologies. Test results of the proposed method shows average sensitivity/specificity/accuracy of 85.43/97.94/95.45 on the 785 retinal fundus images. The proposed method shows an improvement of 14.01% in sensitivity without degrading specificity and accuracy in comparison to the recently published methods.

      PubDate: 2017-02-01T19:25:11Z
      DOI: 10.1016/j.bbe.2016.09.002
       
  • Trends and perspectives in modification of zirconium oxide for a dental
           prosthetic applications – A review
    • Authors: D.S. Nakonieczny; A. Ziębowicz; Z.K. Paszenda; C. Krawczyk
      Abstract: Publication date: Available online 28 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): D.S. Nakonieczny, A. Ziębowicz, Z.K. Paszenda, C. Krawczyk
      Full-ceramic dental restorations made from ZrO2 have become increasingly popular due to their aesthetics and mechanical strength, and are gradually replacing prostheses made of porcelain fused to metal. Nevertheless, due to the variability in the physicochemical properties in a wet environment at elevated temperature, zirconia is quite a controversial material, the use of which in the environment of the mouth is questionable and raises many concerns. The reason for the variability in the physicochemical changes is the martensitic transformation in which metastable phases (β, γ) change into the stable phase (α). For biomedical applications, the most desired is the β-phase. A very unfavourable phenomenon accompanying the martensitic transformation in a wet environment is low temperature degradation, which is an autocatalytic process accelerating negative changes in ZrO2. The aim of this review is a comprehensive study of the degradation phenomenon problems according to prosthetic treatment with a fixed prosthesis and ways to reduce it.

      PubDate: 2017-02-01T19:25:11Z
      DOI: 10.1016/j.bbe.2016.10.005
       
  • Automatic parameters selection of Gabor filters with the imperialism
           competitive algorithm with application to retinal vessel segmentation
    • Authors: Farnaz Farokhian; Chunlan Yang; Hasan Demirel; Shuicai Wu; Iman Beheshti
      Abstract: Publication date: Available online 28 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Farnaz Farokhian, Chunlan Yang, Hasan Demirel, Shuicai Wu, Iman Beheshti
      Retinal images play an important role in the early diagnosis of diseases such as diabetes. In the present study, an automatic image processing technique is proposed to segment retinal blood vessels in fundus images. The technique includes the design of a bank of 180 Gabor filters with varying scale and elongation parameters. Furthermore, an optimization method, namely, the imperialism competitive algorithm (ICA), is adopted for automatic parameter selection of the Gabor filter. In addition, a systematic method is proposed to determine the threshold value for reliable performance. Finally, the performance of the proposed approach is analyzed and compared with that of other approaches on the basis of the publicly available DRIVE database. The proposed method achieves an area under the receiver operating characteristic curve of 0.953 and an average accuracy of up to 0.9392. Thus, the results show that the proposed method is well comparable with alternative methods in the literature.

      PubDate: 2017-02-01T19:25:11Z
      DOI: 10.1016/j.bbe.2016.12.007
       
  • Development of the deterministic and stochastic Markovian model of a
           dendritic neuron
    • Authors: Aleksandra Świetlicka; Karol Gugała; Witold Pedrycz; Andrzej Rybarczyk
      Abstract: Publication date: Available online 28 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Aleksandra Świetlicka, Karol Gugała, Witold Pedrycz, Andrzej Rybarczyk
      In this study, we propose a model of the dendritic structure of the neuron (referred to as a neural network – NN), which can be viewed as an extension of the models that are currently used in the description of the potential on the neuron's membrane. The proposed extensions augment the generic model and offer a fuller description of the neuron's nature. The common assumption being used in most of the previous models stating a single channel (forming component of the neuron's membrane) can be positioned in only one of the two states (permissive – open and non-permissive – closed), is now relaxed by allowing the channel to be positioned in more states (five or eight states). The relationship between these states is expressed in terms of Markov kinetic schemes. In the paper, we demonstrate that the new approach is more suitable for a larger number of applications than the conventional Hodgkin–Huxley model. The study, by providing the mathematical background of the new extended model, forms a significant step towards a hardware implementation of the biologically realistic neural network (NN) of this type. To reduce the number of components required in such implementation, we propose a new optimization technique that significantly reduces the computational complexity of a single neuron.

      PubDate: 2017-02-01T19:25:11Z
      DOI: 10.1016/j.bbe.2016.10.002
       
  • A classification framework for prediction of breast density using an
           ensemble of neural network classifiers
    • Authors: Indrajeet Kumar; H.S. Bhadauria; Jitendra Virmani; Shruti Thakur
      Abstract: Publication date: Available online 27 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Indrajeet Kumar, H.S. Bhadauria, Jitendra Virmani, Shruti Thakur
      The present work proposes a classification framework for the prediction of breast density using an ensemble of neural network classifiers. Expert radiologists, visualize the textural characteristics of center region of a breast to distinguish between different breast density classes. Accordingly, ROIs of fixed size are cropped from the center location of the breast tissue and GLCM mean features are computed for each ROI by varying inter-pixel distance ‘d’ from 1 to 15. The proposed classification framework consists of two stages, (a) first stage: this stage consists of a single 4-class neural network classifier NN0 (B-I/B-II/B-III/B-IV) which yields the output probability vector [P B-I PB-II P B-III P B-IV] indicating the probability values with which a test ROI belongs to a particular breast density class. (b) second stage: this stage consists of an ensemble of six binary neural network classifiers NN1 (B-I/B-II), NN2 (B-I/B-III), NN3 (B-I/B-IV), NN4 (B-II/B-III), NN5 (B-II/B-IV) and NN6 (B-III/B-IV). The output of the first stage of the classification framework, i.e. output on NN0 is used to obtain the two most probable classes for a test ROI. In the second stage this test ROI is passed through one of the binary neural networks, i.e. NN1 to NN6 corresponding to the two most probable classes predicted by NN0. After passing the entire test ROIs through the second stage, the overall accuracy increases from 79.5% to 90.8%. The promising results achieved by the proposed classification framework indicate that it can be used in clinical environment for differentiation between breast density patterns.

      PubDate: 2017-02-01T19:25:11Z
      DOI: 10.1016/j.bbe.2017.01.001
       
  • In vitro test method for the development of intelligent lower limb
           prosthetic devices
    • Authors: Cristiano Marinelli; Hermes Giberti; Ferruccio Resta
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Cristiano Marinelli, Hermes Giberti, Ferruccio Resta
      In recent decades, the technological progress has contributed to the development of appliances that significantly improve human life. The biomedical field has benefited more than others from this innovation process. In particular, robotics advances have led to the development of prostheses that allow who suffered the amputation of a lower limb to walk almost like a healthy person. Although sophisticated, the current solutions are not yet able to completely reestablish the function of their biological counterpart. According to authors’ opinion this deficiency is principally due to the lack of suitable development and verification methods rather than of appropriate technology resources. Therefore, an innovative bench for testing lower limb prostheses considering working conditions more realistic than those defined by the legislation in force is presented in this paper. The mechanical setup is composed of a 6-axis industrial robot and a custom 2-axis active force plate. The first one is used to replicate the movements of the limb residual segment in space. The second one to load the prosthetic foot both in longitudinal and vertical direction, that is, in the sagittal plane. Both the design choices and the operation procedure are illustrated. Then, a numerical model of the bench is developed in order to assess the merits and the limits of the proposed solution.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.003
       
  • Ictal EEG classification based on amplitude and frequency contours of IMFs
    • Authors: K.S. Biju; Hara Abdul Hakkim; M.G. Jibukumar
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): K.S. Biju, Hara Abdul Hakkim, M.G. Jibukumar
      Electroencephalogram (EEG) signal serves is a powerful tool in epilepsy detection. This study decomposes intrinsic mode functions (IMFs) into amplitude envelope and frequency functions on a time-scale basis using the analytic function of Hilbert transform. IMFs results from the empirical mode decomposition of EEG signals. Features such as energy and entropy parameters were calculated from the amplitude contour of each IMF. Other features, such as interquartile range, mean absolute deviation and standard deviation are also computed for their instantaneous frequencies. Discriminative features were extracted using a large database to classify healthy and ictal EEG signals. Normal EEG segments were differentiated from the seizure attack in individual IMF features, multiple features with individual IMF, and individual features with multiple IMFs. Discriminating capability of three Cases was tested. (i) Artificial neural network and (ii) adaptive neuro-fuzzy inference system classification were used to identify EEG segments with seizure attacks. ANOVA was used to analyze statistical performance. Energy and entropy-based features of instantaneous amplitude and standard deviation of instantaneous frequency of IMF2 and IMF1 have 100% accuracy, sensitivity, and specificity. Good performance with a single feature that represents information of the whole data was obtained. The result involved less complicated computation than other time–frequency analysis techniques.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.12.005
       
  • Automatic segmentation of infant brain MR images: With special reference
           to myelinated white matter
    • Authors: Chelli N. Devi; Anupama Chandrasekharan; V.K. Sundararaman; Zachariah C. Alex
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Chelli N. Devi, Anupama Chandrasekharan, V.K. Sundararaman, Zachariah C. Alex
      Automatic segmentation of infant brain images is faced with numerous challenges like poor image contrast, motion artifacts, and changes caused by progressive myelination of the infant brain. Since timely myelination points to normal brain maturity, monitoring the progress and degree of myelination is clinically significant. However, most of the existing segmentation methods do not segment myelinated portions of the infant brain. In this paper, we propose a segmentation approach focused on segmenting the myelinated white matter tissue in T1-weighted magnetic resonance images of the infant brain. The novelty of the algorithm lies in the introduction of a weighted localized Tsallis entropy based thresholding method. The proposed method is also tested on older babies beyond the one-year age mark to verify its utility and robustness. It is seen that the mean Dice coefficients obtained for myelin segmentation by the proposed weighted localized method are higher than that of the other methods, namely, the conventional Tsallis entropy thresholding and modified localized method.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.11.004
       
  • Automatic contrast enhancement of brain MR images using Average Intensity
           Replacement based on Adaptive Histogram Equalization (AIR-AHE)
    • Authors: Iza Sazanita Isa; Siti Noraini Sulaiman; Muzaimi Mustapha; Noor Khairiah A. Karim
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Iza Sazanita Isa, Siti Noraini Sulaiman, Muzaimi Mustapha, Noor Khairiah A. Karim
      Medical imaging is the most established technique of visualizing the interior of the human body without the risk of the non-invasive effect. This technology is designed to produce images, and it is also capable of representing information about the screening location. In MRI imaging, the poor image quality particularly the low contrast image may provide insufficient data for the visual interpretation of such affected locations. Therefore, the need of image enhancement arises to improve image visions and also to computationally support the image processing technique. In general, conventional contrast enhancement methods may work well for some images. However, in MRI brain image, there are often more complex situations where the WMH signal is high but it may mistakenly be considered as other brain tissues such as CSF. With the motivation to classify the most possible WMH regions, this paper proposes a novel image contrast algorithm of WMH enhancement for MRI image. This algorithm is also known as the Average Intensity Replacement – Adaptive Histogram Equalization (AIR-AHE). The proposed algorithm is applied to the FLAIR image based on the intensity adjustment and contrast mapping techniques. The proposed algorithm for the image enhancement is superior to the existing methods by using image evaluation quantitative methods of PSNR, average gradient values and MSE. Furthermore, the edge information pertaining to the potential WMH regions can effectively increase the accuracy of the results.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.12.003
       
  • 3D vascular tree segmentation using a multiscale vesselness function and a
           level set approach
    • Authors: Tomasz Woźniak; Michał Strzelecki; Agata Majos; Ludomir Stefańczyk
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Tomasz Woźniak, Michał Strzelecki, Agata Majos, Ludomir Stefańczyk
      The paper presents a method aimed at segmentation of a vascular network in 3D medical data. The method implements an extended version of a vesselness function that considers multiscale image filtering to emphasize vessels of different diameters. This function is combined with a level set approach based on a Chan–Vese model. The proposed method was evaluated on medical images of the brain and hand vasculature. These images were obtained by different modalities, including angio-CT and two MR acquisition protocols. The proposed technique was quantitatively validated for the tree phantom image by assessing segmentation accuracy and for the angio-CT images by estimating diameters of vessel fragments. Two radiologists provided also qualitative evaluation of this approach. It was demonstrated that this method ensures correct segmentation of a vessel tree in the analyzed images. Moreover, it enables detection of thinner vessel branches when compared to single scale vesselness function approaches.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.11.003
       
  • Optimization of multi-slot coaxial antennas for microwave thermotherapy
           based on the S11-parameter analysis
    • Authors: Piotr Gas
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Piotr Gas
      The underlying aim of presented article is to determine the optimal location and sizes of the air gaps inside a multi-slot coaxial antenna with 50-ohm feed based on the S 11-parameter characteristics of microwave applicator to get the best antenna impedance matching to the treated tissue. The next step is the selection of the levels limits of the antenna input power, for which temperature of the tissue do not exceed known therapeutic elevations for microwave therapies at hyperthermic and ablation temperatures. The proposed approach provides a relatively simple method for optimization of the location and size of slots in the antenna structure. The proper choice of limit values of total antenna input power enables appropriate adjustment of temperature of the target tissue to preserve optimal cancer treatment.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.001
       
  • Reliability of stiffness measurement device during passive isokinetic
           spastic wrist movements of healthy subjects and hemiplegics
    • Authors: Abbas Orand; Hiroyuki Miyasaka; Kotaro Takeda; Genichi Tanino; Takeshi Chihara; Hidehiko Beppu; Shigeru Sonoda
      Abstract: Publication date: Available online 21 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Abbas Orand, Hiroyuki Miyasaka, Kotaro Takeda, Genichi Tanino, Takeshi Chihara, Hidehiko Beppu, Shigeru Sonoda
      The consistency of torque measurements during repetitive moving arm movements and also during passive wrist movements at two angular velocities of slow (∼6°/s) and moderate (∼120°/s) was investigated. The designed and developed device was applied to 3 cases, to a spring, to 8 able-bodied subjects and to 2 hemiplegic patients. While the mean of the intra-class correlation coefficient of subjects were 0.65 and 0.75 for slow and moderate angular velocities, those of the hemiplegic patients and the spring respectively ranged between excellent values of 0.93–1 and 0.91–1. The Pearson's product-moment correlation coefficients of the 3 cases for the 2 slow and moderate angular velocities ranged between 0.80 and 1. We could verify that the device can be used in our future researches and it can (1) reliably rotate a moving arm horizontally with angular velocities between 3 and 350°/s constantly in a range of motion between −60 and 60° and (2) simultaneously capture the data of angular displacement, torque, and two electromyogram activities. For the standardization of our future studies with the device, we could verify the stability of the last two repeated passive wrist movements in case of patients. The results of the study with the able-bodied subjects are also important as a reference for our studies with the hemiplegic.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.001
       
  • Grafting of oxidized carboxymethyl cellulose with hydrogen peroxide in
           presence of Cu(II) to chitosan and biological elucidation
    • Authors: Saida Benghanem; Asma Chetouani; Meriem Elkolli; Mahmoud Bounekhel; Djafar Benachour
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Saida Benghanem, Asma Chetouani, Meriem Elkolli, Mahmoud Bounekhel, Djafar Benachour
      The chemical interaction of chitosan (CS) is performed in the presence of sodium carboxymethyl cellulose (CMC) and/or oxidized CMC. The latter is obtained by the action of H2O2/CuSO4 to generate carbonyl and carboxyl groups which were increased with CuSO4 concentration. The characterization of these new materials is made by FTIR, TGA, XRD and SEM. Examination of the hemolytic potential showed that the hydrogels were non hemolytic in nature. The hydrogels were non-toxic and blood-compatible. The antibacterial and antioxidant activities of samples were investigated.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.003
       
  • Electromyography and mechanomyography signal recognition: Experimental
           analysis using multi-way array decomposition methods
    • Authors: Andrzej Wołczowski; Rafał Zdunek
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Andrzej Wołczowski, Rafał Zdunek
      In this study, we considered the problem of controlling a prosthetic hand with noisy electromyography (EMG) and mechanomyography (MMG) signals. Several dimensionality reduction methods were analyzed to assess their efficiency at classifying these signals, which were registered during the performance of grasping movements with various objects. Using the cross-validation technique, we compared various dimensionality reduction methods, such as principal components analysis, nonnegative matrix factorization, and some tensor decomposition models. The experimental results demonstrated that the highest classification accuracy (exceeding 95% for all subjects when classifying 11 grasping movements) and lowest computational complexity were obtained when higher-order singular value decomposition was applied to a multi-way array of multi-channel spectrograms, where the temporal EMG/MMG signals from all channels were concatenated.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.004
       
  • Verification of the new ‘all ages’ spirometric reference values for
           use in young Polish children of Caucasian origin
    • Authors: Waldemar Tomalak; Jakub Radliński; Halina Bańka; Bożena Myszkal; Krzysztof Żarnowski; Zbigniew Doniec; Jaroslav Fabry
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Waldemar Tomalak, Jakub Radliński, Halina Bańka, Bożena Myszkal, Krzysztof Żarnowski, Zbigniew Doniec, Jaroslav Fabry
      Interpretation of the spirometric results in young children aged 3 years and onward was a difficult task, because existing reference values usually covered age range of 7–18 years. Recently two big studies concerning ‘all ages’ reference equations were published: the study of The Asthma UK Initiative (Stanojevic et al. AJRCCM 2009) and the so called GLI2012 values (Quanjer et al. ERJ 2012); both providing equations with LMS approach for spirometric reference values for age range of 3–95 years. The aim of the study was to test the applicability of the new sets of equations in a group of healthy Polish children of Caucasian descent. The analysis was performed on a data gathered from children admitted to outpatient department for diagnostic reasons. Children performed impulse oscillometry (IOS) measurements and spirometry. Elevated value of oscillometric resistance at 5Hz (R5) eliminated children from analysis as well as forced expiratory time less than 1.5s. Final analysis was performed on results obtained from 142 children aged 4–10 years. Z-scores and percent of predicted values were calculated for FEV1, FVC and FEV1/FVC using both sets; additionally z-score and percent predicted was also calculated for FEV0.75/FVC using Stanojevic's equation. The distribution of all calculated z-scores was normal. For FEV1/FVC mean±SD of z-score was 0.01±0.80 using GLI2012 and −0.15±0.79 using Stanojevic's set. Mean value of percent predicted values using GLI2012 was 100.2±5.5% for FEV1/FVC, 107.4±9.4% for FEV1 and 106.6±10.1% for FVC. Our results confirm applicability of the new sets of reference values in young Caucasian children from Poland and point out the potential diagnostic value of FEV0.75/FVC.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.006
       
  • Bat optimization based neuron model of stochastic resonance for the
           enhancement of MR images
    • Authors: Munendra Singh; Ashish Verma; Neeraj Sharma
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Munendra Singh, Ashish Verma, Neeraj Sharma
      Stochastic resonance (SR) performs the enhancement of the low in contrast image with the help of noise. The present paper proposes a modified neuron model based stochastic resonance approach applied for the enhancement of T1 weighted, T2 weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging (DWI) sequences of magnetic resonance imaging. Multi objective bat algorithm has been applied to tune the parameters of the modified neuron model for the maximization of two competitive image performance indices contrast enhancement factor (F) and mean opinion score (MOS). The quality of processed image depends on the choice of these image performance indices rather the selection of SR parameters. The proposed approach performs well on enhancement of magnetic resonance (MR) images, as a result there is improvement in the gray-white matter differentiation and has been found helpful in the better diagnosis of MR images.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.006
       
  • The intima with early atherosclerotic lesions is load-bearing component of
           human thoracic aorta
    • Authors: Magdalena Kobielarz; Marta Kozuń; Aleksandra Kuzan; Krzysztof Maksymowicz; Wojciech Witkiewicz; Celina Pezowicz
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Magdalena Kobielarz, Marta Kozuń, Aleksandra Kuzan, Krzysztof Maksymowicz, Wojciech Witkiewicz, Celina Pezowicz
      The aim of the study was to evaluate the mechanical properties of the adventitia, media, and especially intima of the human thoracic aortic wall in the early stages of atherosclerosis (stage I to III according to the Stary's classification). Histological and immunohistochemical techniques were used to evaluate the severity of atherosclerosis and the correctness of separation of the respective layers. Circumferential specimens of the adventitia, media, and intima (n =193) were prepared from 27 arteries. The mechanical properties, i.e. the ultimate tensile strength, the maximum strain, and the maximum tangential elastic moduli, were determined in uniaxial tensile test and presented as a median (Me). The tensile strength of the intima (Me=105kPa) is comparable to the media (Me=123kPa) and lower than for the adventitia (Me=808.5kPa). The intima also undergoes the lowest maximum strain (Me=0.008), and its elastic modulus (Me=11,510kPa) is significantly higher compared to the media (Me=5280kPa). Therefore, presented results indicate that even in the early stages of atherosclerotic development the intima takes part in the process of mechanical loads bearing.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.008
       
  • An objective method to identify optimum clip-limit and histogram
           specification of contrast limited adaptive histogram equalization for MR
           images
    • Authors: Justin Joseph; Sivaraman Jayaraman; R. Periyasamy; V.R. Simi
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Justin Joseph, Sivaraman Jayaraman, R. Periyasamy, V.R. Simi
      In contrast limited adaptive histogram equalization (CLAHE), the selection of tile size, clip-limit and the distribution which specify desired shape of the histogram of image tiles is paramount, as it critically influences the quality of the enhanced image. The optimal value of these parameters devolves on the generic of the image to be enhanced and usually they are selected empirically. In this paper, the degradation of intensity, textural and geometric features of the medical image with respect to the variation in clip-limit and specified histogram shape is analyzed. The statistical indices used to quantify the feature degradation are Absolute Mean Brightness Error (AMBE), Absolute Deviation in Entropy (ADE), Peak Signal to Noise Ratio (PSNR), Variance Ratio (VR), Structural Similarity Index Matrix (SSIM) and Saturation Evaluation Index (SEI). The images used for the analysis are axial plane MR images of magnetic resonance spectroscopy (MRS), under gradient recalled echo (GRE), diffusion weighted imaging (DWI) 1000b Array Spatial Sensitivity Encoding Technique (ASSET), T2 Fluid Attenuation Inversion Recovery (FLAIR) and T1 Fast Spin-Echo Contrast Enhanced (FS-ECE) series of pre-operative Glioblastoma-edema complex. The experimental analysis was performed using Matlab®. Results show that for MR images the exponential histogram specification with a clip-limit of 0.01 is found to be optimum. At optimum clip-limit, the mean of SSIM exhibited by the Rayleigh, uniform and exponential histogram specification were found to be 0.7477, 0.7946 and 0.8457, for ten sets of MR images and mean of variance ratio are 1.242, 2.0316 and 1.7711, respectively.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.11.006
       
  • A full reference Morphological Edge Similarity Index to account processing
           induced edge artefacts in magnetic resonance images
    • Authors: P.G. Kuppusamy; Justin Joseph; J. Sivaraman
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): P.G. Kuppusamy, Justin Joseph, J. Sivaraman
      An objective measure of edge similarity between the original and processed images to quantify the processing induced artefacts in medical image computing is proposed in this article. Globally accepted Image Quality Analysis (IQA) indices such as Peak Signal Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) measure the structural similarity between the original and processed image and do not specifically reflect the resemblance of the edge content. Most of the IQA indices either do not comply with the subjective quality ratings or they are prone to noise level. In the proposed Morphological Edge Similarity Index (MESI), the binary edge maps of the reference and processed images are generated via gradient based threshold and these edge maps are objectively compared to yield a reliable edge quality metric. The index is found superior to Edge Preservation Index (EPI), Edge Strength Similarity based Image quality Metric (ESSIM) and SSIM in terms of dynamic variability, correlation with subjective quality ratings, robustness to noise and sensitivity to degradation in edge quality caused by blockiness artefacts in image compression. MESI exhibits a correlation of 0.9985, very close to unity, with the subjective quality ratings. It is useful for objectively evaluating the performance of denoising, sharpening and enhancement schemes and for the selection of optimum value of the arbitrary parameters used in them.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.12.008
       
  • Classification of tactile event-related potential elicited by Braille
           display for brain–computer interface
    • Authors: Junichi Hori; Naoto Okada
      Abstract: Publication date: Available online 20 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Junichi Hori, Naoto Okada
      To construct brain–computer interface (BCI), an event-related potential (ERP) induced by a tactile stimulus is investigated in this paper. For ERP-based BCI, visual or auditory information is frequently used as the stimulus. In the present study, we focus on tactile sensations to reserve their visual and auditory senses for other activities. Several patterns of mechanical tactile stimulation were applied to the index fingers of both hands using two piezo actuators that were used as a braille display. Human experiments based on the oddball paradigm were carried out. Subjects were instructed to pay attention to unusual target stimuli while avoiding other frequent non-target stimuli. The extracted features were classified by applying stepwise linear discriminant analysis. As a result, an accuracy of 85% and 60% were obtained for 2- and 4-class classification, respectively. The accuracy was improved by increasing the number of electrodes even when short stimulus intervals were used.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.10.007
       
  • Bayesian network aided grasp and grip efficiency estimation using a smart
           data glove for post-stroke diagnosis
    • Authors: Debeshi Dutta; Satyanarayan Modak; Anirudh Kumar; Joydeb Roychowdhury; Soumen Mandal
      Abstract: Publication date: Available online 19 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Debeshi Dutta, Satyanarayan Modak, Anirudh Kumar, Joydeb Roychowdhury, Soumen Mandal
      Stroke is one of the major causes behind the increased mortality rate throughout the world and disability among the survivors. Such disabilities include several grasp and grip related impairment in daily activities like holding a glass of water, counting currency notes, producing correct signature in bank, etc., that seek serious attention. Present therapeutic facilities, being expensive and time-consuming, fail to cater the poverty stricken rural class of the society. In this paper, on the basis of an investigation, we developed a smart data glove based diagnostic device for better treatment of such patients by providing timely estimation of their grasp quality. Data collected from a VMG30 motion capture glove for six patients who survived stroke and two other healthy subjects was fused with suitable hypothesis obtained from a domain expert to reflect the required outcome on a Bayesian network. The end result could be made available to a doctor at a remote location through a smart phone for further advice or treatment. Results obtained clearly distinguished a patient from a healthy subject along with supporting estimates to study and compare different grasping gestures. The improvement in mobility could be assessed after physiotherapeutic treatments using the proposed method.

      PubDate: 2017-01-25T20:20:50Z
      DOI: 10.1016/j.bbe.2016.09.005
       
  • Spatial and spatio-temporal filtering based on common spatial patterns and
           Max-SNR for detection of P300 component
    • Authors: Fereshteh Salimian Rizi; Vahid Abootalebi; Mohamad Taghi Sadeghi
      Abstract: Publication date: Available online 18 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Fereshteh Salimian Rizi, Vahid Abootalebi, Mohamad Taghi Sadeghi
      Recent advances in brain-computer interfaces (BCIs) have developed a new arena for designing systems to help disabled persons to communicate with the surrounding environment. P300 speller is one of the most famous BCI systems choosing the characters from a virtual keyboard through the analysis of EEG signals. P300 detection is an important processing step of these systems. The accuracy of P300 detection highly depends on the feature extraction method. In this study, the maximum signal to noise ratio (Max-SNR) has been used for feature extraction, which rarely applied in this area. This study presents a novel feature extraction technique, named spatio-temporal Max-SNR (ST.Max-SNR). Unlike the standard Max-SNR which only uses spatial patterns of a signal, the proposed method, separately consider the spatial and temporal patterns of the signal to enhance the accuracy of feature extraction. Due to the similarity of the common spatial pattern (CSP) and the Max-SNR algorithms, the performance of this technique and its extension, common Spatio-temporal pattern (CSTP), has been compared with the proposed method. Then, the LDA and SWLDA classifiers are used for classification of the features. Our experimental results show that the Max-SNR based spatio-temporal features lead to an average classification accuracy of 94.4 percent suggesting the best performance.

      PubDate: 2017-01-18T18:16:37Z
      DOI: 10.1016/j.bbe.2016.11.001
       
  • An analytical method for the adaptive computation of threshold of gradient
           modulus in 2D anisotropic diffusion filter
    • Authors: Justin Joseph; R. Periyasamy
      Abstract: Publication date: Available online 18 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Justin Joseph, R. Periyasamy
      In spite of the extensive application of Anisotropic Diffusion (AD) filter in software packages for medical image analysis, denoising and edge preservation offered by it depends exclusively on the selection of the value of Threshold of Gradient Modulus (TGM). Tuning the TGM to its optimum value through trial and error is subjective and tiring. An analytical model to compute the optimum value of TGM adaptively from the mean gradient of the image itself is proposed in this article. The qualitative examination of the gradient and true edge maps of the original and restored Magnetic Resonance images revealed that analytically computed TGM ensures best trade-off between noise suppression and edge preservation.

      PubDate: 2017-01-18T18:16:37Z
      DOI: 10.1016/j.bbe.2016.12.002
       
  • Use of siliconised infant endotracheal tubes reduces work of breathing
           under turbulent flow
    • Authors: Barbara Stankiewicz; Marcin Rawicz; Marek Darowski; Krzysztof Zielinski; Maciej Kozarski; Andrzej Chwojnowski
      Abstract: Publication date: Available online 17 January 2017
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Barbara Stankiewicz, Marcin Rawicz, Marek Darowski, Krzysztof Zielinski, Maciej Kozarski, Andrzej Chwojnowski
      The high resistance of an infant endotracheal tube (ETT) can markedly impair ventilation and gas exchange. Since some manufacturers cover the inner surface of their ETTs with a silicon layer in order to diminish deposition and ease mucous evacuation from airway, via surface roughness decrease, we assessed whether the silicon layer may affect tube resistance, work of breathing and other parameters of ventilation. We compared SUMI (Poland) non-siliconised and siliconised polyvinyl chloride ETTs (2.5, 3.0 and 4.0mm ID), twenty of each type and size combination. Simulating volume-controlled ventilation with the hybrid (numerical–physical) lung models of a premature infant and a 3-month-old baby peak inspiratory pressure (PIP), peak inspiratory and expiratory flow (PIF, PEF), (patient+ETT) inspiratory and expiratory airway resistance (R ins, R exp) and work of breathing by ventilator (WOBvt) were measured. Additionally, images of the both type surfaces were taken using Hitachi TM-1000 electron microscope. When 2.5 and 3.0mm ID ETTs were examined, laminar flow (Re <2300) across the tube was observed, and there were no clinically significant differences in the ventilation parameters between non-siliconised and siliconised tubes. Whereas, when 4mm ID ETTs were tested, turbulent flow was observed, and PIP, R ins, R exp and WOBvt were significantly lower (5%, 17%, 17%, and 7%, respectively) (P <0.05), but PIF and PEF were significantly higher (8%, 14%) (P <0.05). Thus, the silicone inner surface of ETT offers less resistance and WOBvt in presence of turbulent flow. However, artifacts observed on the surface of non-siliconised and siliconised ETTs can potentially impair ventilation.

      PubDate: 2017-01-18T18:16:37Z
      DOI: 10.1016/j.bbe.2016.11.002
       
  • Simplification of breast deformation modelling to support breast cancer
           treatment planning
    • Authors: Marta Danch-Wierzchowska; Damian Borys; Barbara Bobek-Billewicz; Michal Jarzab; Andrzej Swierniak
      Pages: 531 - 536
      Abstract: Publication date: 2016
      Source:Biocybernetics and Biomedical Engineering, Volume 36, Issue 4
      Author(s): Marta Danch-Wierzchowska, Damian Borys, Barbara Bobek-Billewicz, Michal Jarzab, Andrzej Swierniak
      The exact delineation of tumour boundaries is of utmost importance in the planning of cancer therapy, either surgery or pre- or post-operative radiation treatment. In the case of breast cancer one of the most advanced modalities is magnetic resonance imaging (MRI). Although MRI scans provide wealth of information about the structure of a tumour and the surrounding tissues, the data obtained represent the patient in a prone position, with breast, in a coil while surgery is performed in a supine position, on lying breast. There is no doubt that a patient's breast in both positions has a different shape and that this influences the intra-breast relations. Our present preliminary study introduces a simple breast model developed from prone images. The model should be built rapidly and by a simple procedure, based only on essential structures, and the goal is to prove its usefulness in treatment planning.

      PubDate: 2016-08-16T12:47:25Z
      DOI: 10.1016/j.bbe.2016.06.001
       
  • Multi-sequence texture analysis in classification of in vivo MR images of
           the prostate
    • Authors: Dorota Duda; Marek Kretowski; Romain Mathieu; Renaud de Crevoisier; Johanne Bezy-Wendling
      Pages: 537 - 552
      Abstract: Publication date: 2016
      Source:Biocybernetics and Biomedical Engineering, Volume 36, Issue 4
      Author(s): Dorota Duda, Marek Kretowski, Romain Mathieu, Renaud de Crevoisier, Johanne Bezy-Wendling
      The aim of the study is to investigate the potential of multi-sequence texture analysis in the characterization of prostatic tissues from in vivo Magnetic Resonance Images (MRI). The approach consists in simultaneous analysis of several images, each acquired under different conditions, but representing the same part of the organ. First, the texture of each image is characterized independently of the others. Then the feature values corresponding to different acquisition conditions are combined in one vector, characterizing a combination of textures derived from several sequences. Three MRI sequences are considered: T1-weighted, T2-weighted, and diffusion-weighted. Their textures are characterized using six methods (statistical and model-based). In total, 30 tissue descriptors are calculated for each sequence. The feature space is reduced using a modified Monte Carlo feature selection, combined with wrapper methods, and Principal Components Analysis. Six classifiers were used in the work. Multi-sequence texture analysis led to better classification results than single-sequence analysis. The subsets of features selected with the Monte Carlo method guaranteed the highest classification accuracies.

      PubDate: 2016-08-16T12:47:25Z
      DOI: 10.1016/j.bbe.2016.05.002
       
  • Generative Model-Driven Feature Learning for dysarthric speech recognition
    • Authors: N. Rajeswari; S. Chandrakala
      Pages: 553 - 561
      Abstract: Publication date: 2016
      Source:Biocybernetics and Biomedical Engineering, Volume 36, Issue 4
      Author(s): N. Rajeswari, S. Chandrakala
      Recognition of speech uttered by severe dysarthric speakers needs a robust learning technique. One of the commonly used generative model-based classifiers for speech recognition is a hidden Markov model. Generative model-based classifiers do not do well for overlapping classes and due to insufficient training data. Dysarthric speech is normally partial or incomplete that leads to improper learning of temporal dynamics. To overcome these issues, we focus on learning features for dysarthric speech recognition that involves recognizing the sequential patterns of varying length utterances. We propose a Generative Model-Driven Feature Learning based discriminative framework that maps the sequence of feature vectors to fixed dimension vector spaces induced by the generative models. The discriminative classifier is built in that vector space. The proposed HMM-based fixed dimensional vector representation provides better discrimination for dysarthric speech than the conventional HMM. We examine the performance of the proposed method to recognize the isolated utterances from the UA-Speech database. The recognition accuracy of the proposed model is better than the conventional hidden Markov model-based approach.

      PubDate: 2016-08-16T12:47:25Z
      DOI: 10.1016/j.bbe.2016.05.003
       
  • Evaluating the fetal heart rate baseline estimation algorithms by their
           influence on detection of clinically important patterns
    • Authors: Janusz Jezewski; Krzysztof Horoba; Dawid Roj; Janusz Wrobel; Tomasz Kupka; Adam Matonia
      Pages: 562 - 573
      Abstract: Publication date: 2016
      Source:Biocybernetics and Biomedical Engineering, Volume 36, Issue 4
      Author(s): Janusz Jezewski, Krzysztof Horoba, Dawid Roj, Janusz Wrobel, Tomasz Kupka, Adam Matonia
      A correctly estimated component of fetal heart rate signal (FHR) – so called baseline – is a precondition for proper recognition of acceleration and deceleration patterns. A number of various algorithms for estimating the FHR baseline was proposed so far. However, there is no reference standard enabling their objective evaluation, and thus no methodology of comparing the different algorithms still exists. In this paper we propose a method for evaluation of automatically determined baseline in reference to a set of experts, based on ten separate groups of signals comprising typical variability patterns observed in the fetal heart rate. As it was proposed earlier [1], the given algorithm is evaluated based on the characteristic patterns detected using the obtained baseline, instead of direct analysis of the baseline shape. For the purpose of quantitative assessment of the estimated baseline a new synthetic inconsistency coefficient was applied. The proposed methodology enabled to evaluate eleven well-known algorithms. We believe that the method will be a valuable tool for assessment of the existing algorithms, as well as for developing the new ones.

      PubDate: 2016-08-16T12:47:25Z
      DOI: 10.1016/j.bbe.2016.06.003
       
  • Early predicting a risk of preterm labour by analysis of antepartum
           electrohysterograhic signals
    • Authors: Krzysztof Horoba; Janusz Jezewski; Adam Matonia; Janusz Wrobel; Robert Czabanski; Michal Jezewski
      Pages: 574 - 583
      Abstract: Publication date: 2016
      Source:Biocybernetics and Biomedical Engineering, Volume 36, Issue 4
      Author(s): Krzysztof Horoba, Janusz Jezewski, Adam Matonia, Janusz Wrobel, Robert Czabanski, Michal Jezewski
      This study is aimed at evaluation of the capability to indicate the preterm labour risk by analysing the features extracted from the signals of electrical uterine activity. Free access database was used with 300 signals acquired in two groups of pregnant women who delivered at term (262 cases) and preterm (38 cases). Signal features comprised classical time domain description, spectral parameters and nonlinear measures of contractile activity. Their mean values were calculated for all the contraction episodes detected in each record and their statistical significance for recognition of two groups of recordings was provided. Obtained results were related to the previous study where the same features were applied but they were determined for entire signals. Influence of electrodes location, band-pass filter settings and gestation week was investigated. The obtained results showed that a spectral parameter – the median frequency was the most promising indicator of the preterm labour risk.

      PubDate: 2016-08-16T12:47:25Z
      DOI: 10.1016/j.bbe.2016.06.004
       
  • Automatic segmentation of cell nuclei using Krill Herd optimization based
           multi-thresholding and Localized Active Contour Model
    • Authors: Sabeena Beevi K.; Madhu S. Nair; G.R. Bindu
      Pages: 584 - 596
      Abstract: Publication date: 2016
      Source:Biocybernetics and Biomedical Engineering, Volume 36, Issue 4
      Author(s): Sabeena Beevi K., Madhu S. Nair, G.R. Bindu
      Analysis of tissue components in histopathology image stays on as the gold standard in detecting different types of cancers. Active Contour Models (ACM) serve as a widely useful tool in object segmentation in pathology images. Since the ACMs are susceptible to initial contour placement, efficiency of object detection is very much influenced by the selection of primary curve placement technique. In this paper, in order to handle diffused intensities present along object boundaries in histopathology images, segmentation of nuclei from breast histopathology images are carried out by Localized Active Contour Model (LACM) utilizing bio-inspired optimization techniques in the detection stage. Krill Herd Algorithm (KHA) based optimal curve placement provides better initial boundaries compared with other detection techniques. The segmentation performance is investigated based on Housdorff (HD) and Maximum Absolute Distance (MAD) measures. The algorithm also shows comparable performance with other state-of-the-art techniques in terms of quantitative measures such as Precision, Accuracy and Touching Nuclei Resolution when applied to complex images of stained breast biopsy slides.

      PubDate: 2016-08-16T12:47:25Z
      DOI: 10.1016/j.bbe.2016.06.005
       
  • MIAP – Web-based platform for the computer analysis of microscopic
           images to support the pathological diagnosis
    • Authors: Tomasz Markiewicz; Anna Korzynska; Andrzej Kowalski; Zaneta Swiderska-Chadaj; Piotr Murawski; Bartlomiej Grala; Malgorzata Lorent; Marek Wdowiak; Jakub Zak; Lukasz Roszkowiak; Wojciech Kozlowski; Dorota Pijanowska
      Pages: 597 - 609
      Abstract: Publication date: 2016
      Source:Biocybernetics and Biomedical Engineering, Volume 36, Issue 4
      Author(s): Tomasz Markiewicz, Anna Korzynska, Andrzej Kowalski, Zaneta Swiderska-Chadaj, Piotr Murawski, Bartlomiej Grala, Malgorzata Lorent, Marek Wdowiak, Jakub Zak, Lukasz Roszkowiak, Wojciech Kozlowski, Dorota Pijanowska
      The aim of the project is to design and to implement a web-based platform for the computer analysis of microscopic images which support the pathological diagnosis. The use of the platform will be free of charge. It offers: quantitative analysis of staining tissue sample’ images, archiving microscopic images, peer consultation, and join work independently from distance between scientific collaborating centers to registered doctors, scientists and students. The use of proposed platform allows: (i) to save pathologists’ time spend on quantitative analysis, (ii) to reduce consulting costs by replacing sending of the physical preparations by placing their images (mostly virtual slide) on the platform server, (iii) to increase reproducibility, comparability and objectivity of quantitative evaluations. These effects have a direct impact on improving the effectiveness and decreasing the costs of patients’ treatment. This paper presents the main ideas of the project which deliver web-based system working as multi-functional, integrated, modular and scalable computer system. The details of hardware solutions, concept of the workflow in the platform, the programming language and interpreters, the specific tools and algorithms, and the user interfaces are described below. The practical solutions for web-based services in the area of medical image analysis, storage and retrieval are also presented and discussed.

      PubDate: 2016-08-16T12:47:25Z
      DOI: 10.1016/j.bbe.2016.06.006
       
  • Feature projection k-NN classifier model for imbalanced and incomplete
           medical data
    • Authors: Piotr Porwik; Tomasz Orczyk; Marcin Lewandowski; Marcin Cholewa
      Abstract: Publication date: Available online 2 November 2016
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Piotr Porwik, Tomasz Orczyk, Marcin Lewandowski, Marcin Cholewa
      Many datasets, especially various historical medical data are incomplete. Various qualities of data can significantly hamper medical diagnosis and are bottlenecks of medical support systems. Nowadays, such systems are often used in medical diagnosis. Even great number of data can be unsuitable when data is imbalanced, missing or corrupted. In some cases these troubles can be overcome by machine learning algorithms designed for predictive modeling. Proposed approach was tested on real medical data and some benchmarks dataset form UCI repository. The liver fibrosis disease from a medical point of view is difficult to treatment and has a significant social and economic impact. Stages of liver fibrosis are diagnosed by clinical observation and evaluations, coupled with a so-called METAVIR rating scale. However, these methods may be insufficient, especially in the recognition of phase of the disease. This paper describes a newly developed algorithm to non-invasive fibrosis stage recognition using machine learning methods – a classification model based on feature projection k-NN classifier. This solution allows extracting data characteristics from the historical data which may be incomplete and may contain imbalance (unequal) sets of patients. Proposed novel solution is based on peripheral blood analysis without using any specialized biomarkers, and can be successfully included to medical diagnosis support systems and might be a powerful tool for effective estimation of liver fibrosis stages.

      PubDate: 2016-11-05T03:37:59Z
      DOI: 10.1016/j.bbe.2016.08.002
       
  • Detection of hard exudates using mean shift and normalized cut method
    • Authors: Sreeparna Banerjee; Diptoneel Kayal
      Abstract: Publication date: Available online 18 October 2016
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Sreeparna Banerjee, Diptoneel Kayal
      As diabetic retinopathy (DR) is one of the main causes of loss of vision among diabetic patients, an early detection using automated detection techniques can prevent blindness among diabetic patients. Hard exudates constitute one of the early symptoms of DR and this paper describes a method for its detection using fundus images of retina, employing a combination of morphological operations, mean shift (MS), normalized cut (NC) and Canny's operation. This combined technique avoids over segmentation and at the same time reduces the time complexity while clearly delineating the exudates. Output of the proposed method is evaluated using public databases and produces sensitivity, specificity and accuracy as 98.80%, 98.25% and 98.65%, respectively. The ROC curve gives 0.984 as area under curve. The sensitivity, specificity, accuracy and area under curve of ROC indicate the effectiveness of the method.

      PubDate: 2016-11-05T03:37:59Z
      DOI: 10.1016/j.bbe.2016.07.001
       
  • Multiclassifier systems applied to the computer-aided sequential medical
           diagnosis
    • Authors: Marek Kurzyński; Marcin Majak; Andrzej Żołnierek
      Abstract: Publication date: Available online 4 October 2016
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Marek Kurzyński, Marcin Majak, Andrzej Żołnierek
      The diagnosis of patient's state based on results of successive examinations is common task in the medicine. In computer-aided algorithms taking into account the patient's history in order to improve the quality of classification seems to be very reasonable solution. In this study, two original multiclassifier systems (MC) for the computer-aided sequential diagnosis are developed, which differ with decision scheme and the methods of combining of base classifiers. The first MC system is based on dynamic ensemble selection scheme and works in two-level structure. The second MC system in combining procedure uses original concept of meta-Bayes classifier and produces decision according to the Bayes rule. Both MC systems were practically applied to the diagnosis of human acid–base equilibrium states and compared with some state-of-the-art sequential diagnosis methods. Results obtained in experimental investigations imply that MC system is effective approach, which improves recognition accuracy in sequential diagnosis scheme.

      PubDate: 2016-10-08T09:24:31Z
      DOI: 10.1016/j.bbe.2016.08.001
       
  • Automated detection of uterine contractions in tocography signals –
           Comparison of algorithms
    • Authors: Krzysztof Horoba; Janusz Wrobel; Janusz Jezewski; Tomasz Kupka; Dawid Roj; Michal Jezewski
      Abstract: Publication date: Available online 4 October 2016
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Krzysztof Horoba, Janusz Wrobel, Janusz Jezewski, Tomasz Kupka, Dawid Roj, Michal Jezewski
      Monitoring of uterine contractile activity enables to control the progress of labor. Automated detection of contractions is an integral part of the signal analysis implemented in computer-aided fetal surveillance system. Comparison of four algorithms for automated detection of uterine contractions in the signal of uterine mechanical activity is presented. Three algorithms are based generally on analysis of the frequency distribution of signal values. The fourth method relies on analyzing the rate of changes of the uterine activity signal. The reference data in form of beginning and end of contraction episodes were provided by human experts. Obtained results show that all algorithms were capable to detect above 91% reference contractions, and less than 7% of recognized patterns were false. Two algorithms can be distinguished as providing a higher performance expressed by the sensitivity of 95% and the positive predictive value of 97%. Such results could be obtained by optimization of contraction validation criteria.

      PubDate: 2016-10-08T09:24:31Z
      DOI: 10.1016/j.bbe.2016.08.005
       
  • Early stage of chronic kidney disease by using statistical evaluation of
           the previous measurement results
    • Authors: Selahaddin Batuhan Akben
      Abstract: Publication date: Available online 4 October 2016
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Selahaddin Batuhan Akben
      Chronic kidney disease (CKD) that causes the progressive losses in kidney functions is one of the major public health problems. Expert medical doctors can diagnose the CKD through symptoms, blood and urine tests in its early stages. However, the diagnosis of CKD might be difficult for expert medical doctors in case of the questionable measurement result. Therefore a new mathematical method that would be helpful to the expert medical doctors is required. It can be said that there is no studies related with automatic diagnosis of CKD in the literature. This study aims to remedy this shortcoming in the literature. In this study, for each of test and symptom values, averages of measurement results were calculated separately for healthy subjects and patients. Then the measured values were marked as “0” or “1” (healthy or patient) according to closeness to average values. Finally, the classification was performed by averaging the values marked for each subject. The success rate of the proposed method is between 96% and 98% according to the age ranges. In conclusion section of the study, how to implement the proposed method in real life is offered.

      PubDate: 2016-10-08T09:24:31Z
      DOI: 10.1016/j.bbe.2016.08.004
       
  • Development of a fuzzy-driven system for ovarian tumor diagnosis
    • Authors: Patryk Żywica; Krzysztof Dyczkowski; Andrzej Wójtowicz; Anna Stachowiak; Sebastian Szubert; Rafał Moszyński
      Abstract: Publication date: Available online 2 October 2016
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Patryk Żywica, Krzysztof Dyczkowski, Andrzej Wójtowicz, Anna Stachowiak, Sebastian Szubert, Rafał Moszyński
      In this paper we present OvaExpert, an intelligent system for ovarian tumor diagnosis. We give an overview of its features and main design assumptions. As a theoretical framework the system uses fuzzy set theory and other soft computing techniques. This makes it possible to handle uncertainty and incompleteness of the data, which is a unique feature of the developed system. The main advantage of OvaExpert is its modular architecture which allows seamless extension of system capabilities. Three diagnostic modules are described, along with examples. The first module is based on aggregation of existing prognostic models for ovarian tumor. The second presents the novel concept of an Interval-Valued Fuzzy Classifier which is able to operate under data incompleteness and uncertainty. The third approach draws from cardinality theory of fuzzy sets and IVFSs and leads to a bipolar result that supports or rejects certain diagnoses.

      PubDate: 2016-10-08T09:24:31Z
      DOI: 10.1016/j.bbe.2016.08.003
       
  • An attempt to localize brain electrical activity sources using EEG with
           limited number of electrodes
    • Authors: Andrzej Majkowski; Łukasz Oskwarek; Marcin Kołodziej; Remigiusz J. Rak
      Abstract: Publication date: Available online 29 July 2016
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Andrzej Majkowski, Łukasz Oskwarek, Marcin Kołodziej, Remigiusz J. Rak
      A very interesting research goal is to find underlying sources generating the EEG signal – referred to as the “EEG inverse problem”. Its aim is to determine spatial distribution of brain activity, described by local brain currents density, on the basis of potentials measured on the scalp as EEG signal. The purpose of the research presented in the article was to check whether the results of the inverse problem solution, obtained by the LORETA algorithm for the reduced set of 8 electrodes selected by the authors will be close to the results for the initial set of 32 electrodes. EEG signals were registered during the BCI operation based on ERD/ERS potentials. Obtained results showed no significant differences in the location of the most important sources in both cases. It is worth emphasizing that reducing the number of electrodes would have a significant impact on an BCI ergonomics.

      PubDate: 2016-08-16T12:47:25Z
      DOI: 10.1016/j.bbe.2016.07.002
       
  • Automated object and image level classification of TB images using support
           vector neural network classifier
    • Authors: Ebenezer Priya; Subramanian Srinivasan
      Abstract: Publication date: Available online 9 July 2016
      Source:Biocybernetics and Biomedical Engineering
      Author(s): Ebenezer Priya, Subramanian Srinivasan
      In this work, digital Tuberculosis (TB) images have been considered for object and image level classification using Multi Layer Perceptron (MLP) neural network activated by Support Vector Machine (SVM) learning algorithm. The sputum smear images are recorded under standard image acquisition protocol. The TB objects which include bacilli and outliers in the considered images are segmented using active contour method. The boundary of the segmented objects is described by fifteen Fourier Descriptors (FDs). The prominent FDs are selected using fuzzy entropy measures. These selected FDs of the TB objects are fed as input to the SVM learning algorithm of the MLP Neural Network (SVNN) and the result is compared with the state-of-the-art approach, Back Propagation Neural Network (BPNN). Results show that the segmentation method identifies the bacilli which retain their shape in-spite of artifacts present in the images. The methodology adopted has significantly enhanced the SVNN accuracy to 91.3% for object and 92.5% for image level classification than BPNN.

      PubDate: 2016-08-16T12:47:25Z
      DOI: 10.1016/j.bbe.2016.06.008
       
  • Multi-step process in computer assisted diagnosis of posterior cruciate
           ligaments
    • Authors: Zarychta
      Abstract: Publication date: Available online 30 June 2016
      Source:Biocybernetics and Biomedical Engineering
      Author(s): P. Zarychta
      A multi-step methodology resulting in a three-dimensional visualization and construction of feature vector of posterior cruciate ligament is presented. In the first step the location of the posterior cruciate ligament is established using the fuzzy image concept. The fuzzy image concept is based on the entropy measure of fuzziness extended to two dimensions. In order to reduce the area of analysis, the region of interest including the ligament structures is detected. In this case, the fuzzy C-means algorithm with median modification helping to reduce blurred edges was implemented. After finding the region of interest, the fuzzy connectedness procedure was performed. This procedure permitted to extract the ligament structures. On the basis of the extracted posterior cruciate ligament structures, the three-dimensional visualization of this ligament was built and, with the support of experts’ knowledge, an appropriate feature vector was constructed and its values assigned for normal and pathological cases. Correct results were obtained for over 88% of 97 cases.

      PubDate: 2016-08-16T12:47:25Z
       
 
 
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