Gibbs prior models, marching cubes, and deformable models: A hybrid framework for 3D medical image segmentation

Lecture Notes in Computer Science

Volumn 2879, Issue PART 2, 2003, Pages 703-710

  Chen, Ting ^a   Metaxas, Dimitris ^b  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOUNDARY CONDITIONS; BRAIN; COMPUTATIONAL METHODS; IMAGE SEGMENTATION; MATHEMATICAL MODELS; OBJECT RECOGNITION; PARAMETER ESTIMATION; SIGNAL TO NOISE RATIO; THEOREM PROVING; TUMORS; COMPUTATIONAL EFFICIENCY; DEFORMATION; EFFICIENCY; GEOMETRY; MEDICAL COMPUTING; MEDICAL IMAGE PROCESSING; MEDICAL IMAGING;

GIBBS MODELS; HYBRID FRAMEWORKS; MARCHING CUBES; MEDICAL ANIMATION; DEFORMABLE MODELING; DEFORMATION RESULT; GIBBS PRIOR MODELS; GRADIENT INFORMATIONS; HIGH-QUALITY SEGMENTATION; MANUAL SEGMENTATION; SEGMENTATION METHODS; SEGMENTATION RESULTS;

MEDICAL IMAGING; IMAGE SEGMENTATION;

EID: 0345255151     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39903-2_86     Document Type: Conference Paper

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