Segmentation with a shape dictionary

Abstract

Image segmentation plays an important role in many medical applications. Automatic segmentation algorithms are challenged by low SNR and significant artifacts resulting from motion and signal voids. In this study, we propose a novel level set based segmentation method with a shape dictionary. Unlike previous studies that use a single template or probabilistic models, we propose to construct a shape dictionary and model the shape prior as sparse combinations of shape templates in the dictionary. The proposed method generated promising segmentation results on low SNR MR images, even with signal voids.