A Computer-Aided system for Discrimination of Dilated Cardiomyopathy Using Echocardiographic Images

In this paper, the discrimination of ultrasonic heart (echocardiographic) images is studied by making use of some texture features, including the angular second moment, contrast, correlation and entropy which are obtained from a gray-level cooccurrence matrix. Features of these types are used as inputs to the input layer of a neural network (NN) to classify two sets of echocardiographic images-normal heart and dilated cardiomyopathy (DCM) (18 and 13 samples, respectively). The performance of the NN classifier is also compared to that of a minimum distance (MD) classifier. Implementation of our algorithm is performed on a PC-486 personal computer. Our results show that the NN produces about 94% (the confidence level setting is 0.9) and the MD produces about 84% correct classification. We notice that the NN correctly classifies all the DCM cases, namely, all the misclassified cases are of false positive. These results indicate that the method of feature-based image analysis using the NN has potential utility for computer-aided diagnosis of the DCM and other heart diseases