Statistically constrained snake deformations

In this paper we present a method for constraining the deformations of Snakes (Active Contour Models) when segmenting a known class of objects. The method we propose is similar to both Active Shape Models (ASM) but without the landmark identification and correspondence requirement, and to Active Contour Models (ACM), but armed with a priori information about shape variation. Rather than representing the object boundary by spatial landmarks in a point-by-point fashion, we employ a frequency-based boundary representation. In this way, the Principal Component Analysis (PCA), which is central to ASM, is applied to a set of frequency-domain shape descriptors, removing the need for the difficult determination of spatial landmarks. Given a training set of representative images of the object of interest, we extract an average object shape along with a set of significant shape variation modes, explaining most of the shape variation in the training set. Armed with this a priori model of shape variation we find the boundaries in unknown images by placing an initial ACM and allowing it to deform only according to the examined shape variations. The described methodology was applied to a set of 105 echocardiographic images for locating the left ventricular boundary. The results were particularly encouraging in clinically difficult cases where the ventricular boundary was partly occluded by noise.