A COMPUTER-AIDED SYSTEM FOR DISCRIMINATION OF DILATED CARDIOMYOPATHY USING ECHOCARDIOGRAPHIC IMAGES

In this paper, the discrimination of ultrasonic heart (echocardiograph ic) images is studied by making use of some texture features, includin g the angular second moment, contrast, correlation and entropy which a re obtained from a gray-level cooccurrence matrix. Features of these t ypes are used as inputs to the input layer of a neural network (NN) to classify two sets of echocardiographic images - normal heart and dila ted cardiomyopathy (DCM) (18 and 13 samples, respectively). The perfor mance of the NN classifier is also compared to that of a minimum dista nce (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 8 4% correct classification. We notice that the NN correctly classifies all the DCM cases, namely, all the misclassified cases are of false po sitive. These results indicate that the method of feature-based image analysis using the NN has potential utility for computer-aided diagnos is of the DCM and other heart diseases.