title: Incorporating shape prior into active contours with a sparse linear combination of training shapes: application to corpus callosum segmentation.
abstract: In this paper, a novel method of embedding shape information into level set image segmentation is proposed. Our method is based on inferring shape variations by a sparse linear combination of instances in the shape repository. Given a sufficient number of training shapes with variations, a new shape can be approximated by a linear span of training shapes associated with those variations. At each step of curve evolution the curve is moved to minimize Chan-Vese energy functional as well as toward the best approximation based on a linear combination of training samples. Although the method is general, in this paper it has been applied to the problem of segmentation of corpus callosum from 2D sagittal MR images.
authors.forename: Mohammad M
authors.initials: MM
authors.lastname: Farhangi
authors.forename: Hichem
authors.initials: H
authors.lastname: Frigui
authors.forename: Robert
authors.initials: R
authors.lastname: Bert
authors.forename: Amir A
authors.initials: AA
authors.lastname: Amini
mesh_terms: D003337:Corpus Callosum / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging