Detecting left ventricular endocardial and epicardial boundaries by digital two-dimensional echocardiography.
Cardiac function is evaluated using echocardiographic analysis of shape attributes, such as the heart wall thickness or the shape change of the heart wall boundaries. This requires that the complete boundaries of the heart wall be detected from a sequence of two-dimensional ultrasonic images of the heart. The image segmentation process is made difficult since these images are plagued by poor intensity contrast and dropouts caused by the intrinsic limitations of the image formation process. Current studies often require having trained operators manually trace the heart walls. A review of previous work is presented, along with how this problem can be viewed in the context of the computer vision area. A novel algorithm is presented for detecting the boundaries. This algorithm first detects spatially significant features based on the measurement of image intensity variations. Since the detection step suffers from false alarms and missing boundary points, further processing uses high-level knowledge about the heart wall to label the detected features for noise rejection and to fill in the missing points by interpolation.