This paper proposes an intelligent framework to accurately analyze these echo images in order to discover disease category and assess the severity automatically. Typically, each video consists of 90-100 frames of 2D echo, color Doppler image video, and several .jpg images. Our framework consists of parallel algorithms developed under OpenMP environment. The major tasks are cardiac boundary tracing, quantifying the heart chambers and extracting 2D features, and other features required for computing statistical features and build a classifier model for categorization. Segmentation of an echo image is done using parallel implementation of K-Means algorithm and they are boundary extracted using active contour method. Bayesian model is used to classify a given patient into normal or abnormal. The experiment involves videos taken from 60 normal and abnormal patients from a local cardiology Hospital. The results obtained with our algorithms outperformed with respect to the results that have already been reported. © Springer International Publishing Switzerland 2014.
cited By (since 1996)0; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@bec2d9e ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@3ea6e62f Through org.apache.xalan.xsltc.dom.DOMAdapter@3f5237cb; Conference Code:107333
Sa Nandagopalan, Sudarshan, T. S. Bb, Deepak, Nb, and Pradeep, Nb, “Intelligent Echocardiographic Video Analyzer Using Parallel Algorithms”, Advances in Intelligent Systems and Computing, vol. 265 AISC, pp. 157-166, 2014.