A local adjustment strategy for the initialization of dynamic causal modelling to infer effective connectivity in brain epileptic structures
This paper addresses the question of effective connectivity in the human cerebral cortex in the context of epilepsy. Among model based approaches to infer brain connectivity, spectral Dynamic Causal Modelling is a conventional technique for which we propose an alternative to estimate cross spectral density. The proposed strategy we investigated tackles the sub-estimation of the free energy using the well-known variational Expectation-Maximization algorithm highly sensitive to the initialization of the parameters vector by a permanent local adjustment of the initialization process. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 6, 2017 Category: Bioinformatics Authors: Wentao Xiang, Ahmad Karfoul, Huazhong Shu, R égine Le Bouquin Jeannès Source Type: research
A Novel Computer-Aided Diagnosis System for Breast MRI Based on Feature Selection and Ensemble Learning
Breast cancer is a common cancer among women. With the development of modern medical science and information technology, medical imaging techniques have an increasingly important role in the early detection and diagnosis of breast cancer. In this paper, we propose an automated computer-aided diagnosis (CADx) framework for magnetic resonance imaging (MRI). The scheme consists of an ensemble of several machine learning-based techniques, including ensemble under-sampling (EUS) for imbalanced data processing, the Relief algorithm for feature selection, the subspace method for providing data diversity, and Adaboost for improvin...
Source: Computers in Biology and Medicine - March 3, 2017 Category: Bioinformatics Authors: Wei Lu, Zhe Li, Jinghui Chu Source Type: research
Correlation between transversal and orthogonal maximal diameters of abdominal aortic aneurysms and alternative rupture risk predictors
There is no standard for measuring maximal diameter (Dmax) of abdominal aortic aneurysm (AAA) from computer tomography (CT) images although differences between Dmax evaluated from transversal (axialDmax) or orthogonal (orthoDmax) planes can be large especially for angulated AAAs. Therefore we investigated their correlations with alternative rupture risk indicators as peak wall stress (PWS) and peak wall rupture risk (PWRR) to decide which Dmax is more relevant in AAA rupture risk assessment. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 3, 2017 Category: Bioinformatics Authors: Kamil Novak, Stanislav Polzer, Tomas Krivka, Robert Vlachovsky, Robert Staffa, Lubos Kubicek, Lukas Lambert, Jiri Bursa Source Type: research
Local gray level S-curve transformation – A generalized contrast enhancement technique for medical images
Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 2, 2017 Category: Bioinformatics Authors: Akash Gandhamal, Sanjay Talbar, Suhas Gajre, Ahmad Fadzil M. Hani, Dileep Kumar Source Type: research
Local Gray Level S-Curve Transformation − A Generalized Contrast Enhancement Technique for Medical Images
Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 2, 2017 Category: Bioinformatics Authors: Akash Gandhamal, Sanjay Talbar, Suhas Gajre, Ahmad Fadzil M. Hani, Dileep Kumar Source Type: research
Gold-standard for computer-assisted morphological sperm analysis
We describe a gold-standard for morphological sperm analysis (SCIAN-MorphoSpermGS), a dataset of sperm head images with expert-classification labels in one of the following classes: normal, tapered, pyriform, small or amorphous. This gold-standard is for evaluating and comparing known techniques and future improvements to present approaches for classification of human sperm heads for semen analysis. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 2, 2017 Category: Bioinformatics Authors: Violeta Chang, Alejandra Garc ía, Nancy Hitschfeld, Steffen Härtel Source Type: research
Fun cube based brain gym cognitive function assessment system
The aim of this study is to design and develop a fun cube (FC) based brain gym (BG) cognitive function assessment system using the wireless sensor network and multimedia technologies. The system comprised (1) interaction devices, FCs and a workstation used as interactive tools for collecting and transferring data to the server, (2) a BG information management system responsible for managing the cognitive games and storing test results, and (3) a feedback system used for conducting the analysis of cognitive functions to assist caregivers in screening high risk groups with mild cognitive impairment. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - March 2, 2017 Category: Bioinformatics Authors: Tao Zhang, Chung-Chih Lin, Tsang-Chu Yu, Jing Sun, Wen-Chuin Hsu, Alice May-Kuen Wong Source Type: research
Development of an Automaton Model of Rotational Activity Driving Atrial Fibrillation
Atrial fibrillation (AF) is difficult to treat effectively, owing to uncertainty in where to best ablate to eliminate arrhythmogenic substrate. A model providing insight into the electrical activation events would be useful to guide catheter ablation strategy.MethodA two-dimensional, 576 ×576 node automaton was developed to simulate atrial electrical activity. The substrate field was altered by the presence of differing refractory period at varying locations. Fibrosis was added in the form of short, randomly positioned lines of conduction block. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - February 28, 2017 Category: Bioinformatics Authors: E.J. Ciaccio, A.B. Biviano, E.Y. Wan, N.S. Peters, H. Garan Source Type: research
Quantitative Glioma Grading Using Transformed Gray-Scale Invariant Textures of MRI
A computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - February 24, 2017 Category: Bioinformatics Authors: Kevin Li-Chun Hsieh, Cheng-Yu Chen, Chung-Ming Lo Source Type: research
Machine learning in the prediction of cardiac epicardial and mediastinal fat volumes
We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in computed tomography images using regression algorithms. The obtained results indicate that it is feasible to predict these fats with a high degree of correlation, thus alleviating the requirement for manual or automatic segmentation of both fat volumes. Instead, segmenting just one of them suffices, while the volume of the other may be predicted fairly precisely. The correlation coefficient obtained by the Rotation Forest algorithm using MLP Regressor for predicting the mediastinal fat based on the epicardial fat was 0.9876, with a re...
Source: Computers in Biology and Medicine - February 23, 2017 Category: Bioinformatics Authors: É.O. Rodrigues, V.H.A. Pinheiro, P. Liatsis, A. Conci Source Type: research
Cardiac epicardial and mediastinal fat volumes correlate: the feasibility of predicting one based on the other
We propose a methodology to predict the cardiac epicardial and mediastinal fat volumes in Computed Tomography images using regression algorithms. We conclude that it is feasible to predict these fats with a high degree of correlation, thus alleviating the requirement for manual or automatic segmentation of both fat volumes. Instead, segmenting just one of them suffices, while the volume of the other may be predicted fairly precisely. The correlation coefficient obtained by the Rotation Forest algorithm using the MLP Regressor in predicting the mediastinal fat based on the epicardial fat is 0.9876, with a relative absolute ...
Source: Computers in Biology and Medicine - February 23, 2017 Category: Bioinformatics Authors: É.O. Rodrigues, V.H.A. Pinheiro, P. Liatsis, A. Conci Source Type: research
Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm
We developed a novel computer-aided diagnosis (CAD) system that uses feature-ranking and a genetic algorithm to analyze structural magnetic resonance imaging data; using this system, we can predict conversion of mild cognitive impairment (MCI)-to-Alzheimer's disease (AD) at between one and three years before clinical diagnosis. The CAD system was developed in four stages. First, we used a voxel-based morphometry technique to investigate global and local gray matter (GM) atrophy in an AD group compared with healthy controls (HCs). (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - February 23, 2017 Category: Bioinformatics Authors: Iman Beheshti, Hasan Demirel, Hiroshi Matsuda, for the Alzheimer's Disease Neuroimaging Initiative Source Type: research
Pipeline for inferring protein function from dynamics using coarse-grained molecular mechanics forcefield
Dynamics is integral to the function of proteins, yet the use of molecular dynamics (MD) simulation as a technique remains under-explored for molecular function inference. This is more important in the context of genomics projects where novel proteins are determined with limited evolutionary information. Recently we (Bhadra and Pal, 2014; Proteins: Structure, Function, and Bioinformatics 82: 2443 –2454; DOI: 10.1002/prot.24609) developed a method to match the query protein's flexible segments to infer function using a novel approach combining analysis of residue fluctuation-graphs and auto-correlation vectors derived fro...
Source: Computers in Biology and Medicine - February 22, 2017 Category: Bioinformatics Authors: Pratiti Bhadra, Debnath Pal Source Type: research
An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions
Introduction: Researchers have recently discovered that Diabetes Mellitus can be detected through non-invasive computerized method. However, the focus has been on facial block color features. In this paper, we extensively study the effects of texture features extracted from facial specific regions at detecting Diabetes Mellitus using eight texture extractors.Materials and Methods: The eight methods are from four texture feature families: (1) statistical texture feature family: Image Gray-scale Histogram, Gray-level Co-occurance Matrix, and Local Binary Pattern, (2) structural texture feature family: Voronoi Tessellation, (...
Source: Computers in Biology and Medicine - February 20, 2017 Category: Bioinformatics Authors: Ting Shu, Bob Zhang, Yuan Yan Tang Source Type: research
A new optical flow model for motor unit conduction velocity estimation in multichannel surface EMG
Many studies have demonstrated the feasibility and benefits of Conduction Velocity (CV) estimation from surface electromyograms (EMGs) in various experimental conditions. Among them, a method based on optical flow was proposed recently, demonstrating relatively accurate CV estimation for EMG signals acquired in monopolar mode. We extended this method by a new data model that compensates more realistically for the spatial Motor Unit Action Potential (MUAP) shape variability and enables accurate CV estimation also in single-differential acquisition mode. (Source: Computers in Biology and Medicine)
Source: Computers in Biology and Medicine - February 20, 2017 Category: Bioinformatics Authors: Bo židar Potoènik, Aleš Holobar Source Type: research
