Adaptively learning local shape statistics for prostate segmentation in ultrasound

IEEE Transactions on Biomedical Engineering

Volumn 58, Issue 3 PART 1, 2011, Pages 633-641

  Yan, Pingkun ^a   Xu, Sheng ^b   Turkbey, Baris ^c   Kruecker, Jochen ^b  

^a XI AN INSTITUTE OF OPTICS AND PRECISION MECHANICS   (China)

^b PHILIPS RESEARCH LABORATORIES   (Netherlands)

^c NATIONAL CANCER INSTITUTE   (United States)

Author keywords

Deformable model; prostate; segmentation; shape statistics; transrectal ultrasound (TRUS)

Indexed keywords

DEFORMABLE MODELS; PROSTATE; SEGMENTATION; SHAPE STATISTICS; TRANSRECTAL ULTRASOUND;

ALGORITHMS; DEFORMATION; IMAGE SEGMENTATION; POPULATION STATISTICS; ULTRASONICS;

OBJECT RECOGNITION;

ALGORITHM; ARTICLE; DIAGNOSTIC IMAGING; HUMAN; LEARNING; PROSTATE; TRANSRECTAL ULTRASONOGRAPHY;

ALGORITHMS; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MALE; MODELS, STATISTICAL; PRINCIPAL COMPONENT ANALYSIS; PROSTATE;

EID: 79952138691     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2010.2094195     Document Type: Article

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