Sparse patch based prostate segmentation in CT images

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

Volumn 7512 LNCS, Issue , 2012, Pages 385-392

  Liao, Shu ^a   Gao, Yaozong ^a,b   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b Brooks Computer Science Building   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE SEGMENTATION; MEDICAL COMPUTING; MEDICAL IMAGING; PATIENT TREATMENT; UROLOGY;

DISCRIMINATIVE FEATURES; IMAGE-GUIDED RADIATION THERAPY; LABEL PROPAGATION; PATIENT SPECIFIC; PROSTATE SEGMENTATION; SEGMENTATION ACCURACY; SPARSE REPRESENTATION; STATE OF THE ART;

COMPUTERIZED TOMOGRAPHY;

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

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

MLCS; MLOWN;

EID: 84872943332     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33454-2_48     Document Type: Conference Paper

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