Comput Methods Programs Biomed - Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images.

Tópicos

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Resumo

Coronary Artery Disease (CAD), caused by the buildup of plaque on the inside of the coronary arteries, has a high mortality rate. To efficiently detect this condition from echocardiography images, with lesser inter-observer variability and visual interpretation errors, computer based data mining techniques may be exploited. We have developed and presented one such technique in this paper for the classification of normal and CAD affected cases. A multitude of grayscale features (fractal dimension, entropies based on the higher order spectra, features based on image texture and local binary patterns, and wavelet based features) were extracted from echocardiography images belonging to a huge database of 400 normal cases and 400 CAD patients. Only the features that had good discriminating capability were selected using t-test. Several combinations of the resultant significant features were used to evaluate many supervised classifiers to find the combination that presents a good accuracy. We observed that the Gaussian Mixture Model (GMM) classifier trained with a feature subset made up of nine significant features presented the highest accuracy, sensitivity, specificity, and positive predictive value of 100%. We have also developed a novel, highly discriminative HeartIndex, which is a single number that is calculated from the combination of the features, in order to objectively classify the images from either of the two classes. Such an index allows for an easier implementation of the technique for automated CAD detection in the computers in hospitals and clinics.

Resumo Limpo

coronari arteri diseas cad caus buildup plaqu insid coronari arteri high mortal rate effici detect condit echocardiographi imag lesser interobserv variabl visual interpret error comput base data mine techniqu may exploit develop present one techniqu paper classif normal cad affect case multitud grayscal featur fractal dimens entropi base higher order spectra featur base imag textur local binari pattern wavelet base featur extract echocardiographi imag belong huge databas normal case cad patient featur good discrimin capabl select use ttest sever combin result signific featur use evalu mani supervis classifi find combin present good accuraci observ gaussian mixtur model gmm classifi train featur subset made nine signific featur present highest accuraci sensit specif posit predict valu also develop novel high discrimin heartindex singl number calcul combin featur order object classifi imag either two class index allow easier implement techniqu autom cad detect comput hospit clinic

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