Automatic detection of dilated cardiomyopathy in cardiac ultrasound videos.

In this paper we address the problem of automatic detection of dilated cardiomyopathy from cardiac ultrasound videos. Specifically, we present a new method of robustly locating the left ventricle by using the key idea that the region closest to the apex in a 4-chamber view is the left ventricular region. For this, we locate a region of interest containing the heart in an echocardiogram image using the bounding lines of the viewing sector to locate the apex of the heart. We then select low intensity regions as candidates, and find the low intensity region closest to the apex as the left ventricle. Finally, we refine the boundary by averaging the detection across the heart cycle using the successive frames of the echocardiographic video sequence. By extracting eigenvalues of the shape to represent the spread of the left ventricle in both length and width and augmenting it with pixel area, we form a small set of robust features to discriminate between normal and dilated left ventricles using a support vector machine classifier. Testing of the method of a collection of 654 patient cases from a dataset used to train echocardiographers has revealed the promise of this automated approach to detecting dilated cardiomyopathy in echocardiography video sequences.