Computerized analysis for classification of heart diseases in echocardiographic images

The classification 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 three sets of echocardiographic images: normal heart, dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) (18, 13 and 10 samples, respectively). The performance of the NN classifier is compared to that of decision-theoretic (D-T) method. Moreover, fractal dimension (FD) is also used as an independent feature of images for discrimination. Our results show that the NN, D-T method and FD method produce about 94%, 89%, and 84% correct classification, respectively. The 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