Premium
Statistical shape priors for level set segmentation
Author(s) -
Cremers Daniel
Publication year - 2007
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.200700148
Subject(s) - prior probability , segmentation , artificial intelligence , embedding , clutter , segmentation based object categorization , scale space segmentation , pattern recognition (psychology) , image segmentation , computer science , level set (data structures) , computer vision , image (mathematics) , set (abstract data type) , mathematics , bayesian probability , telecommunications , radar , programming language
Abstract Starting in the early 1990's level set methods have become a popular mathematical framework for variational image segmentation. In many applications of segmentation, however, cost functionals which merely take into account the intensity information of the input image will not give rise to the desired segmentation results. To cope with missing or misleading image information, researchers have proposed to impose statistical shape priors into the segmentation process. Such shape priors favor the evolving embedding function to remain similar to embedding functions associated with a collection of training shapes. As a consequence, one can obtain shape‐consistent segmentation despite large amounts of noise, background clutter and partial occlusion of the object of interest. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)