Echocardiographic Contour Extraction with Local and Global Priors Through Boosting and Level Sets

Abstract

Extracting endocardium and epicardium from echocardiographic images is a challenging task because of large amounts of noise, signal drop-out, unrelated structures, and unseen wall parts. This paper introduces a new technique that automatically extracts cardiac borders by incorporating local and global priors through boosting and level set methods. The shape-based global prior is incorporated into the system by regularly re-initializing the level set surface under the influence of the expert detected contours. The local priors with image and temporal information are learned through boosting. The proposed system has many advantages. First, boosting encodes the knowledge about the image information and the temporal cardiac wall motion effectively by using spatiotemporal filters. Second, the local priors can use any features from the images including different filters and intensity profiles. Furthermore, other hard constraints like local shape, texture, distance, etc. can be added to local features effectively. The system is validated on echocardiograms and the results are found to be promising.