title: Combinative multi-scale level set framework for echocardiographic image segmentation.
abstract: In the automatic segmentation of echocardiographic images, a priori shape knowledge has been used to compensate for poor features in ultrasound images. This shape knowledge is often learned via an off-line training process, which requires tedious human effort and is highly expertise-dependent. More importantly, a learned shape template can only be used to segment a specific class of images with similar boundary shape. In this paper, we present a multi-scale level set framework for segmentation of endocardial boundaries at each frame in a multiframe echocardiographic image sequence. We point out that the intensity distribution of an ultrasound image at a very coarse scale can be approximately modeled by Gaussian. Then we combine region homogeneity and edge features in a level set approach to extract boundaries automatically at this coarse scale. At finer scale levels, these coarse boundaries are used to both initialize boundary detection and serve as an external constraint to guide contour evolution. This constraint functions similar to a traditional shape prior. Experimental results validate this combinative framework.
authors.affiliation: Department of Electrical Engineering, Yale University, BML 322, PO Box 208042, New Haven, CT 06520-8042, USA. ning.lin@yale.edu
authors.forename: Ning
authors.initials: N
authors.lastname: Lin
authors.forename: Weichuan
authors.initials: W
authors.lastname: Yu
authors.forename: James S
authors.initials: JS
authors.lastname: Duncan
mesh_terms: D000465:Algorithms; D016009:Chi-Square Distribution; D004452:Echocardiography / Q000379:methods*; D006331:Heart Diseases / Q000000981:diagnostic imaging*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional