Graph cuts framework for kidney segmentation with prior shape constraints

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4791 LNCS, Issue PART 1, 2007, Pages 384-392

  Ali, Asem M ^a   Farag, Aly A ^a   El Baz, Ayman S ^a  

^a University of Louisville   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; GRAPH THEORY; IMAGE SEGMENTATION; MATHEMATICAL MODELS; POISSON DISTRIBUTION; PROBABILITY;

GRAPH CUTS FRAMEWORK; GRAY LEVEL; IMAGE APPEARANCE MODELS; SHAPE INFORMATION;

BIOLOGICAL ORGANS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; DIFFUSION WEIGHTED IMAGING; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; KIDNEY; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DIFFUSION MAGNETIC RESONANCE IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; KIDNEY; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 38149125527     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-75757-3_47     Document Type: Conference Paper

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