Abstract
In this paper, the classification of echocardiographic images is studied by making use of some texture features, including the angular second moment, the contrast, the correlation, and the entropy which are obtained from a gray-level coocurrence matrix. Features of these types are used to classify two sets of echocardiographic images?normal and abnormal (cardiomyopathy) hearts (18 and 13 samples, respectively). The minimum distance classifier and the evaluation index are employed to evaluate the performance of these features. Implementation of our algorithm is performed on a PC-386 personal computer and produces about 90% correct classification for the two sets of echocardiographic images. Our preliminary results suggest that this method of feature-based image analysis has potential use for computer-aided diagnosis of heart diseases.
