Abstract
In this paper, the discrimination of ultrasonic heart (echocardiographic) images is studied by making use of texture features which are obtained from a gray-level cooccurrence matrix. Features of these types are used as inputs to the input layer of a neutral network (NN) to discriminate three sets of echocardiographic imagesnormal heart, dilated cardiomyopathy (DCM), and hypertrophic cardiomyopathy (HCM) (18,13, Vol.13 No.2 (1996) 61 and 6 samples, respectively). The performance of the NN classifier is compared to that of a minimum distance (MD) classifier and fractal dimension (FD). Our results show that the NN produces about 83%, the MD and FD produce about 76% and 70% correct discrimination (three outputs), respectively. These results indicate that the method of feature-based image analysis using the NN has potential utility for computer-aided diagnosis of the DCM, HCM, and other heart diseases.
