Precise segmentation of multiple organs in CT volumes using learning-based approach and information theory

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

Volumn 7511 LNCS, Issue , 2012, Pages 462-469

  Lu, Chao ^a,c   Zheng, Yefeng ^a   Birkbeck, Neil ^a   Zhang, Jingdan ^a   Kohlberger, Timo ^a   Tietjen, Christian ^b   Boettger, Thomas ^b   Duncan, James S ^c   Zhou, S Kevin ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b SIEMENS AG   (Germany)

^c Yale University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; COMPUTER AIDED DIAGNOSIS; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; INFORMATION THEORY; LEARNING SYSTEMS; MEDICAL COMPUTING; MEDICAL IMAGING; TEXTURES; UROLOGY;

IMAGE GUIDED RADIOTHERAPY; JENSEN-SHANNON DIVERGENCE; LEARNING TECHNIQUES; LEARNING-BASED APPROACH; MARGINAL SPACE LEARNING; ORGAN SEGMENTATION; SEGMENTATION ACCURACY; SEGMENTATION PERFORMANCE;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGE QUALITY; METHODOLOGY; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING; VISCERA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGING, THREE-DIMENSIONAL; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED; VISCERA;

EID: 84988810020     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33418-4_57     Document Type: Conference Paper

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