Computer Methods and Programs in Biomedicine RSS feed: Current Issue. To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; edipemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.http://www.cmpbjournal.com/?rss=yesElsevier Inc.en © 2010 Published by Elsevier Inc. All rights reserved. Computer Methods and Programs in Biomedicine0169-2607992August 2010 © 2010 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.cmpbjournal.com/article/PIIS0169260710001550/abstract?rss=yesEditorial Board10.1016/S0169-2607(10)00155-0Computer Methods and Programs in Biomedicine 99, 2 (2010)2010-08-01Computer Methods and Programs in Biomedicine2010-08-01992S0169-2607(10)X0008-6CO2CO2http://www.cmpbjournal.com/article/PIIS0169260709002946/abstract?rss=yesAbstract: Due to processing constraints, automatic image-based registration of medical images has been largely used as a pre-operative tool. We propose a novel method named sort and count for efficient parallelization of mutual information (MI) computation designed for massively multi-processing architectures. Combined with a parallel transformation implementation and an improved optimization algorithm, our method achieves real-time (less than 1s) rigid registration of 3D medical images using a commodity graphics processing unit (GPU). This represents a more than 50-fold improvement over a standard implementation on a CPU. Real-time registration opens new possibilities for development of improved and interactive intraoperative tools that can be used for enhanced visualization and navigation during an intervention.Parallel computation of mutual information on the GPU with application to real-time registration of 3D medical imagesRamtin Shams, Parastoo Sadeghi, Rodney Kennedy, Richard Hartley10.1016/j.cmpb.2009.11.004Computer Methods and Programs in Biomedicine 99, 2 (2010)2010-08-01Computer Methods and Programs in Biomedicine2010-08-01992S0169-2607(10)X0008-6Section I. Methodology133146http://www.cmpbjournal.com/article/PIIS0169260709002934/abstract?rss=yesAbstract: In this study, a pattern recognition system is presented for improving the classification accuracy of MS-spectra by means of gathering information from different MS-spectra intensity regions using a majority vote ensemble combination. The method starts by automatically breaking down all MS-spectra into common intensity regions. Subsequently, the most informative features (m/z values), which might constitute potential significant biomarkers, are extracted from each common intensity region over all the MS-spectra and, finally, normal from ovarian cancer MS-spectra are discriminated using a multi-classifier scheme, with members the Support Vector Machine, the Probabilistic Neural Network and the k-Nearest Neighbour classifiers. Clinical material was obtained from the publicly available ovarian proteomic dataset (8-7-02). To ensure robust and reliable estimates, the proposed pattern recognition system was evaluated using an external cross-validation process. The average overall performance of the system in discriminating normal from cancer ovarian MS-spectra was 97.18% with 98.52% mean sensitivity and 94.84% mean specificity values.An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectraPanagiotis Bougioukos, Dimitris Glotsos, Dionisis Cavouras, Antonis Daskalakis, Ioannis Kalatzis, Spiros Kostopoulos, George Nikiforidis, Anastasios Bezerianos10.1016/j.cmpb.2009.11.003Computer Methods and Programs in Biomedicine 99, 2 (2010)2010-08-01Computer Methods and Programs in Biomedicine2010-08-01992S0169-2607(10)X0008-6Section I. Methodology147153http://www.cmpbjournal.com/article/PIIS0169260709002995/abstract?rss=yesAbstract: Properly representing the heterogeneous distribution of bone tissue material properties is a key step in constructing subject-specific finite element (FE) bone models from computed tomography (CT) data. Conventional methods represent heterogeneity by subjectively grouping bone of similar attenuation together. A new technique characterizing the level of heterogeneity with an objective metric is presented. This technique identifies the minimal level of heterogeneity needed for an accurate FE model.Subject-specific models of the distal femur and proximal tibia were used in this study. An innovative application of an image processing technique in the context of material properties modeling was introduced to facilitate an objective grouping strategy, which gathered together bone based not only on density but also on location thus capturing the natural variation of bone density seen in CT images.A fully heterogeneous model containing unique material properties for each finite element was not necessary to generate an appropriate solution. Von Mises stress, strain energy density, and nodal displacements were predicted within 5% accuracy using a simplified FE femur model containing less than half the number of bone groups of the fully heterogeneous model. Each group contained attenuations varying less than 20% from the group mean. A substantial computational time savings of 60% was gained with the application of the new technique to assign bone mechanical properties.Representation of bone heterogeneity in subject-specific finite element models for kneeAnthony G. Au, Adrian B. Liggins, V. James Raso, Jason Carey, A. Amirfazli10.1016/j.cmpb.2009.11.009Computer Methods and Programs in Biomedicine 99, 2 (2010)2010-08-01Computer Methods and Programs in Biomedicine2010-08-01992S0169-2607(10)X0008-6Section I. Methodology154171http://www.cmpbjournal.com/article/PIIS0169260709003034/abstract?rss=yesAbstract: We propose a fast alternative to B-splines in image processing based on an approximate calculation using precomputed B-spline weights. During B-spline indirect transformation, these weights are efficiently retrieved in a nearest-neighbor fashion from a look-up table, greatly reducing overall computation time. Depending on the application, calculating a B-spline using a look-up table, called B-LUT, will result in an exact or approximate B-spline calculation. In case of the latter the obtained accuracy can be controlled by the user. The method is applicable to a wide range of B-spline applications and has very low memory requirements compared to other proposed accelerations. The performance of the proposed B-LUTs was compared to conventional B-splines as implemented in the popular ITK toolkit for the general case of image intensity interpolation. Experiments illustrated that highly accurate B-spline approximation can be obtained all while computation time is reduced with a factor of 5–6. The B-LUT source code, compatible with the ITK toolkit, has been made freely available to the community.B-LUT: Fast and low memory B-spline image interpolationDavid Sarrut, Jef Vandemeulebroucke10.1016/j.cmpb.2009.11.013Computer Methods and Programs in Biomedicine 99, 2 (2010)2010-08-01Computer Methods and Programs in Biomedicine2010-08-01992S0169-2607(10)X0008-6Section I. Methodology172178http://www.cmpbjournal.com/article/PIIS0169260710001100/abstract?rss=yesAbstract: Automatic classification of electrocardiogram (ECG) signals is vital for clinical diagnosis of heart disease. This paper investigates the design of an efficient system for recognition of the premature ventricular contraction from the normal beats and other heart diseases. This system includes three main modules: denoising module, feature extraction module and classifier module. In the denoising module, it is proposed the stationary wavelet transform for noise reduction of the electrocardiogram signals. In the feature extraction module a proper combination of the morphological-based features and timing interval-based features are proposed. As the classifier, several supervised classifiers are investigated; they are: a number of multi-layer perceptron neural networks with different number of layers and training algorithms, support vector machines with different kernel types, radial basis function and probabilistic neural networks. Also, for comparison the proposed features, we have considered the wavelet-based features. It has done comprehensive simulations in order to achieve a high efficient system for ECG beat classification from 12 files obtained from the MIT–BIH arrhythmia database. Simulation results show that best results are achieved about 97.14% for classification of ECG beats.Classification of the electrocardiogram signals using supervised classifiers and efficient featuresAtaollah Ebrahim Zadeh, Ali Khazaee, Vahid Ranaee10.1016/j.cmpb.2010.04.013Computer Methods and Programs in Biomedicine 99, 2 (2010)2010-08-01Computer Methods and Programs in Biomedicine2010-08-01992S0169-2607(10)X0008-6Section I. Methodology179194http://www.cmpbjournal.com/article/PIIS016926070900282X/abstract?rss=yesAbstract: Arterial blood gas (ABG) analyses are essential for assessing the acid–base status and guiding the adjustment of mechanical ventilation in critically ill patients. Conventional ABG sampling requires repeated arterial punctures or the insertion of an arterial catheter causing pain, haemorrhage and thrombosis to the patients. Less invasive and non-invasive blood gas analysers, with a technology still in transition, have offered some promise in the recent years. SOPAVent (Simulation of Patients under Artificial Ventilation) is a five compartment blood gas model which captures the basic features of respiratory physiology and gas exchange in the human lungs. It uses ventilator settings and routinely monitored physiological parameters as inputs to produce steady-state estimates of the patient's ABG. This paper overviews the original SOPAVent model and presents an improved data-driven hybrid model that is patient-specific and gives continuous and totally non-invasive ABG predictions. The model has been comprehensively tested in simulations and validated using recorded measurements of ABG and ventilator parameters from ICU patients.Intelligent model-based advisory system for the management of ventilated intensive care patients: Hybrid blood gas patient modelA. Wang, M. Mahfouf, G.H. Mills, G. Panoutsos, D.A. Linkens, K. Goode, H.F. Kwok, M. Denaï10.1016/j.cmpb.2009.09.011Computer Methods and Programs in Biomedicine 99, 2 (2010)2010-08-01Computer Methods and Programs in Biomedicine2010-08-01992S0169-2607(10)X0008-6Section II. Systems and Programs195207http://www.cmpbjournal.com/article/PIIS0169260710000611/abstract?rss=yesAbstract: The optimisation of ventilatory support is a crucial issue for the management of respiratory failure in critically ill patients, aiming at improving gas exchange while preventing ventilator-induced dysfunction of the respiratory system. Clinicians often rely on their knowledge/experience and regular observation of the patient's response for adjusting the level of respiratory support. Using a similar data-driven decision-making methodology, an adaptive model-based advisory system has been designed for the clinical monitoring and management of mechanically ventilated patients. The hybrid blood gas patient model SOPAVent developed in Part I of this paper and validated against clinical data for a range of patients lung abnormalities is embedded into the advisory system to predict continuously and non-invasively the patient's respiratory response to changes in the ventilator settings. The choice of appropriate ventilator settings involves finding a balance among a selection of fundamentally competing therapeutic decisions. The design approach used here is based on a goal-directed multi-objective optimisation strategy to determine the optimal ventilator settings that effectively restore gas exchange and promote improved patient's clinical conditions. As an initial step to its clinical validation, the advisory system's closed-loop stability and performance have been assessed in a series of simulations scenarios reconstructed from real ICU patients data. The results show that the designed advisory system can generate good ventilator-setting advice under patient state changes and competing ventilator management targets.Intelligent model-based advisory system for the management of ventilated intensive care patients. Part II: Advisory system design and evaluationAng Wang, Mahdi Mahfouf, Gary H. Mills, G. Panoutsos, D.A. Linkens, K. Goode, Hoi-Fei Kwok, Mouloud Denaï10.1016/j.cmpb.2010.03.009Computer Methods and Programs in Biomedicine 99, 2 (2010)2010-08-01Computer Methods and Programs in Biomedicine2010-08-01992S0169-2607(10)X0008-6Section II. Systems and Programs208217