Computers in Biology and Medicine RSS feed: Current Issue. Computers in Biology and Medicine is a medium of international communication of the revolutionary advances being made in the application of the computer to the fields of bioscience and medicine. The Journal encourages the exchange of important research, instruction, ideas and information on all aspects of the rapidly expanding area of computer usage in these fields. The Journal will focus on such areas as (1) Analysis of Biomedical Systems: Solutions of Equations; (2) Synthesis of Biomedical Systems: Simulations; (3) Special Medical Data Processing Methods; (4) Special Purpose Computers and Clinical Data Processing for Real Time, Clinical and Experimental Use; and (5) Medical Diagnosis and Medical Record Processing. Also included are the fields of (6) Biomedical Engineering; and (7) Medical Informatics as well as Bioinformatics. The journal is expanding to include (8) Medical Applications of the Internet and World Wide Web; (9) Human Genomics; (10) Proteomics; and (11) Functional Brain Studies. The publication policy is to publish (1) new, original articles that have been appropriately reviewed by competent scientific people, (2) surveys of developments in the fields, (3) pedagogical papers covering specific areas of interest, and (4) book reviews pertinent to the field. Articles which examine the following topics of special interest are being featured in Computers in Biology and Medicine: computer aids to the analysis of biochemical systems, computer aids to biocontrol-systems engineering, neuronal simulation by digital-computer gating components, automatic computer analysis of pictures of biological and medical importance, use of computers by commercial pharmaceutical and chemical organizations, radiation-dosage computers, and accumulating and recalling individual medical records, real-time languages, interfaces to patient monitors, clinical chemistry equipment, data handling and display in nuclear medicine and therapy.http://www.journals.elsevierhealth.com/periodicals/cbm/?rss=yesElsevier Inc.en © 2009 Published by Elsevier Inc. All rights reserved. Computers in Biology and Medicine0010-48253912December 2009 © 2009 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001875/abstract?rss=yesEditorial Board & Publication information10.1016/S0010-4825(09)00187-5Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-2IFCIFChttp://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001620/abstract?rss=yesAbstract: Female breast cancer is the major cause of cancer-related deaths in western countries. Efforts in computer vision have been made in order to help improving the diagnostic accuracy by radiologists. In this paper, we present a methodology that uses Moran's index and Geary's coefficient measures in breast tissues extracted from mammogram images. These measures are used as input features for a support vector machine classifier with the purpose of distinguishing tissues between normal and abnormal cases as well as classifying them into benign and malignant cancerous cases. The use of both proposed techniques showed to be very promising, since we obtained an accuracy of 96.04% and Az ROC of 0.946 with Geary's coefficient and an accuracy of 99.39% and Az ROC of 1 with Moran's index to discriminate tissues in mammograms as normal or abnormal. We also obtained accuracy of 88.31% and Az ROC of 0.804 with Geary's coefficient and accuracy of 87.80% and Az ROC of 0.89 with Moran's index to discriminate tissues in mammograms as benign and malignant.Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVMGeraldo Braz Junior, Anselmo Cardoso de Paiva, Aristófanes Corrêa Silva, Alexandre Cesar Muniz de Oliveira10.1016/j.compbiomed.2009.08.009Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-210631072http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001632/abstract?rss=yesAbstract: In the present paper a number of techniques were applied to determine the effects of epileptic seizure on spontaneous ongoing EEG. The idea is that seizure represents transitions of an epileptic brain from its normal (chaotic) state to an abnormal (more ordered) state. Some nonlinear measures including correlation dimension, maximum Lyapunov exponent and wavelet entropy and a graphical tool, named recurrence plot, as well as a novel technique that collects some statistics of the state space organization were used to characterize interictal, preictal and ictal states and derivate a phase transition. The novelty of this work includes of introducing new types of indicators base upon some nonlinear features besides of proposing a new feature of point distribution in phase space. Our results show that (1) these three states are separable in 3-D feature space of nonlinear measures with a gradual decrease of their quantity in seizure evolution, (2) strong rhythmicity, which manifests in recurrence plots and recurrence quantification analysis measures, appears in dynamic while having entered into seizure and (3) different volumes of state space are occupied during each phase of epileptic disorder.The significance of the work is that this information is a step into the detection of a preictal state and consequently is helpful in the prediction and control of epileptic seizures.On the discrimination of patho-physiological states in epilepsy by means of dynamical measuresSomayeh Raiesdana, Seyed Mohammad Reza Hashemi Golpayegani, Seyed Mohammad P Firoozabadi, Jafar Mehvari Habibabadi10.1016/j.compbiomed.2009.09.001Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-210731082http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001644/abstract?rss=yesAbstract: Individuals following stroke exhibit altered muscle activation and movement patterns. Improving the efficiency of gait can be facilitated by knowing which muscles are affected and how they contribute to the pathological pattern. In this paper we present an electromyographically (EMG) driven musculoskeletal model to estimate muscle forces and joint moments. Subject specific EMG for the primary ankle plantar and dorsiflexor muscles, and joint kinematics during walking for four subjects following stroke were used as inputs to the model to predict ankle joint moments during stance. The model's ability to predict the joint moment was evaluated by comparing the model output with the moment computed using inverse dynamics. The model did predict the ankle moment with acceptable accuracy, exhibiting an average R2 value ranging between 0.87 and 0.92, with RMS errors between 9.7% and 14.7%. The values are in line with previous results for healthy subjects, suggesting that EMG-driven modeling in this population of patients is feasible. It is our hope that such models can provide clinical insight into developing more effective rehabilitation therapies and to assess the effects of an intervention.An EMG-driven model to estimate muscle forces and joint moments in stroke patientsQi Shao, Daniel N. D.N. Bassett, Kurt Manal, Thomas S. T.S. Buchanan10.1016/j.compbiomed.2009.09.002Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-210831088http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001656/abstract?rss=yesAbstract: Regarding the great potential of dual binding site inhibitors of acetylcholinesterase as the future potent drugs of Alzheimer's disease, this study was devoted to extraction of the most effective structural features of these inhibitors from among a large number of quantitative descriptors. To do this, we adopted a unique approach in quantitative structure–activity relationships. An efficient feature selection method was emphasized in such an approach, using the confirmative results of different routine and novel feature selection methods. The proposed methods generated quite consistent results ensuring the effectiveness of the selected structural features.A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesteraseEbrahim Barzegari E.B. Asadabadi, Parviz Abdolmaleki, Seyyed Mohsen Hosseini S.M.H. Barkooie, Samad Jahandideh, Mohammad Ali M.A. Rezaei10.1016/j.compbiomed.2009.09.003Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-210891095http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001668/abstract?rss=yesAbstract: Quantitative evaluation of histopathological features is not only vital for precise characterization of any precancerous condition but also crucial in developing automated computer aided diagnostic system. In this study segmentation and classification of sub-epithelial connective tissue (SECT) cells except endothelial cells in oral mucosa of normal and OSF conditions has been reported. Segmentation has been carried out using multi-level thresholding and subsequently the cell population has been classified using support vector machine (SVM) based classifier. Moreover, the geometric features used here have been observed to be statistically significant, which enhance the statistical learning potential and classification accuracy of the classifier. Automated classification of SECT cells characterizes this precancerous condition very precisely in a quantitative manner and unveils the opportunity to understand OSF related changes in cell population having definite geometric properties. The paper presents an automated classification method for understanding the deviation of normal structural profile of oral mucosa during precancerous changes.Automated classification of cells in sub-epithelial connective tissue of oral sub-mucous fibrosis—An SVM based approachM. Muthu Rama Krishnan, Mousumi Pal, Suneel K S.K. Bomminayuni, Chandan Chakraborty, Ranjan Rashmi R.R. Paul, Jyotirmoy Chatterjee, Ajoy K. A.K. Ray10.1016/j.compbiomed.2009.09.004Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-210961104http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS001048250900167X/abstract?rss=yesAbstract: This research describes a novel monophasic action potential (MAP) annotation algorithm coupled with an interactive graphical user interface (GUI). This algorithm incorporates a number of features to reduce error. Additionally, the GUI has several convenient features to view and manipulate the annotation visually. We analyzed data from swine and human hearts in normal sinus rhythm, during myocardial ischemia, and while eliciting high rates. Validation results indicate correlation >90% between human and computer measurements. This analysis system has several clinical applications in electrophysiological interventions, pharmacodynamic therapies, ischemia detection, and/or in assessment of the time course of electrical activation.An interactive graphical user interface for comprehensive analysis of human and swine cardiac monophasic action potentialManeesh Shrivastav, Paul A. Iaizzo10.1016/j.compbiomed.2009.09.005Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-211051116http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001760/abstract?rss=yesAbstract: To investigate the nature and mechanisms of decompression sickness (DCS), we developed a system for evaluating the success of decompression models in predicting DCS probability from empirical data. Model parameters were estimated using maximum likelihood techniques. Exact integrals of risk functions and tissue kinetics transition times were derived. Agreement with previously published results was excellent including: (a) maximum likelihood values within one log-likelihood unit of previous results and improvements by re-optimization; (b) mean predicted DCS incidents within 1.4% of observed DCS; and (c) time of DCS occurrence prediction. Alternative optimization and homogeneous parallel processing techniques yielded faster model optimization times.A computationally advantageous system for fitting probabilistic decompression models to empirical dataLaurens E. L.E. Howle, Paul W. Weber, Richard D. Vann10.1016/j.compbiomed.2009.09.006Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-211171129http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001772/abstract?rss=yesAbstract: The main purpose of this paper is to demonstrate the capability of fetal phonocardiographic measurements to indicate some congenital heart defects. It deals with the results of investigations carried out during the last four years involving 820 pregnant women. During the investigations fetal cardiac murmurs presenting typical waveforms and incidences of acoustic signals were recorded. Causes of these murmurs are suggested based on comparison with the well-known waveforms of infants and children. A sophisticated signal processing method for murmur discovery is presented, that is also useful for automatic perinatal screening after the 28th week of gestation. By these means low-risk population may also be fully tested for cardiac malfunctions.Computer method for perinatal screening of cardiac murmur using fetal phonocardiographyF. Kovács, N. Kersner, K. Kádár, G. Hosszú10.1016/j.compbiomed.2009.10.001Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-211301136http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001814/abstract?rss=yesAbstract: A completely automated system for the identification of pleural nodules in low-dose and thin-slice computed tomography (CT) of the lung has been developed. The directional-gradient concentration method has been applied to the pleura surface and combined with a morphological opening-based procedure to generate a list of nodule candidates. Each nodule candidate is characterized by 12 morphological and textural features, which are analyzed by a rule-based filter and a neural classifier. This detection system has been developed and validated on a dataset of 42 annotated CT scans. The k-fold cross validation has been used to evaluate the neural classifier performance. The system performance variability due to different ground truth agreement levels is discussed. In particular, the poor 44% sensitivity obtained on the ground truth with agreement level 1 (nodules annotated by only one radiologist) with six FP per scan grows up to the 72% if the underlying ground truth is changed to the agreement level 2 (nodules annotated by two radiologists).Pleural nodule identification in low-dose and thin-slice lung computed tomographyA. Retico, M.E. M.E. Fantacci, I. Gori, P. Kasae, B. Golosio, A. Piccioli, P. Cerello, G. De Nunzio, S. Tangaro10.1016/j.compbiomed.2009.10.005Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-211371144http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS0010482509001826/abstract?rss=yesAbstract: Photoplethysmography (PPG)-based temporal analyses have been widely used as a useful analytical method in physiological and cardiovascular diagnosis. Most of temporal approaches of PPG are based on detected peak points, peak and foot of PPG. The aim of presented study is the development of improved peak detection algorithm of PPG waveform. The present study demonstrates a promising approach to overcome respiration effect and to detect PPG peak. More extensive investigation is necessary to adapt for the cardiovascular diseases, whose PPG morphology has different form.Adaptive threshold method for the peak detection of photoplethysmographic waveformHang Sik H.S. Shin, Chungkeun Lee, Myoungho Lee10.1016/j.compbiomed.2009.10.006Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-211451152http://www.journals.elsevierhealth.com/periodicals/cbm/article/PIIS001048250900184X/abstract?rss=yesAbstract: MRI segmentation is a process of deriving semantic information from volume data. For brain MRI data, segmentation is initially performed at a voxel level and then continued at a brain surface level by generating its approximation. While successful most of the time, automated brain segmentation may leave errors which have to be removed interactively by editing individual 2D slices. We propose an approach for correcting these segmentation errors in 3D modeling space. We actively use the brain surface, which is estimated (potentially wrongly) in the automated FreeSurfer segmentation pipeline. It allows us to work with the whole data set at once, utilizing the context information and correcting several slices simultaneously. Proposed heuristic editing support and automatic visual highlighting of potential error locations allow us to substantially reduce the segmentation time. The paper describes the implementation principles of the proposed software tool and illustrates its application.Interactive surface-guided segmentation of brain MRI dataKonstantin Levinski, Alexei Sourin, Vitali Zagorodnov10.1016/j.compbiomed.2009.10.008Computers in Biology and Medicine 39, 12 (2009)2009-12-01Computers in Biology and Medicine2009-12-013912S0010-4825(09)X0013-211531160