Graph search with appearance and shape information for 3-D prostate and bladder segmentation

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

Volumn 6363 LNCS, Issue PART 3, 2010, Pages 172-180

  Song, Qi ^a   Liu, Yinxiao ^a   Liu, Yunlong ^a   Saha, Punam K ^a   Sonka, Milan ^a,b   Wu, Xiaodong ^a,b  

^a IA United States   (United States)

^b UNIVERSITY OF IOWA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEST FIT; DISTANCE CONSTRAINTS; GRAPH SEARCH; GRAPH-THEORETIC; INTENSITY DISTRIBUTION; LARGE DEFORMATIONS; LOW-ORDER POLYNOMIALS; MAXIMUM FLOWS; MEDICAL IMAGES; NOVEL METHODS; QUANTITATIVE RESULT; REGION INFORMATION; SHAPE INFORMATION; SHAPE PRIORS; SOFT TISSUE; TARGET SURFACE; WEIGHTED GRAPH;

GRAPH THEORY; IMAGE SEGMENTATION; MEDICAL COMPUTING; MEDICAL IMAGING; POLYNOMIAL APPROXIMATION;

THREE DIMENSIONAL;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BLADDER; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGE QUALITY; MALE; METHODOLOGY; PROSTATE; PROSTATE TUMOR; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; HUMANS; IMAGING, THREE-DIMENSIONAL; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATE; PROSTATIC NEOPLASMS; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED; URINARY BLADDER;

EID: 84865763809     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15711-0_22     Document Type: Conference Paper

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