Cardiac disease recognition in echocardiograms using spatio-temporal statistical models

In this paper we present a method of automatic disease recognition by using statistical spatio-temporal disease models in cardiac echo videos. Starting from echo videos of known viewpoints as training data, we form a statistical model of shape and motion information within a cardiac cycle for each disease. Specifically, an active shape model (ASM) is used to model shape and texture information in an echo frame. The motion information derived by tracking ASMs through a heart cycle is then represented compactly using eigen-motion features to constitute a joint spatio-temporal statistical model per disease class and observation viewpoint. Each of these models is then fit to a new cardiac echo video of an unknown disease, and the best fitting model is used to label the disease class. Results are presented that show the method can discriminate patients with hypokinesia from normal patients.