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A Multiple Object Geometric Deformable Model for Image Segmentation.
MedLine Citation:
PMID:  23316110     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Deformable models are widely used for image segmentation, most commonly to find single objects within an image. Although several methods have been proposed to segment multiple objects using deformable models, substantial limitations in their utility remain. This paper presents a multiple object segmentation method using a novel and efficient object representation for both two and three dimensions. The new framework guarantees object relationships and topology, prevents overlaps and gaps, enables boundary-specific speeds, and has a computationally efficient evolution scheme that is largely independent of the number of objects. Maintaining object relationships and straightforward use of object-specific and boundary-specific smoothing and advection forces enables the segmentation of objects with multiple compartments, a critical capability in the parcellation of organs in medical imaging. Comparing the new framework with previous approaches shows its superior performance and scalability.
Authors:
John A Bogovic; Jerry L Prince; Pierre-Louis Bazin
Publication Detail:
Type:  JOURNAL ARTICLE    
Journal Detail:
Title:  Computer vision and image understanding : CVIU     Volume:  117     ISSN:  1077-3142     ISO Abbreviation:  Comput Vis Image Underst     Publication Date:  2013 Feb 
Date Detail:
Created Date:  2013-1-14     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9889342     Medline TA:  Comput Vis Image Underst     Country:  -    
Other Details:
Languages:  ENG     Pagination:  145-157     Citation Subset:  -    
Affiliation:
Johns Hopkins University, Baltimore, MD, USA.
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