A Feature-based learning framework for accurate prostate localization in CT images

IEEE Transactions on Image Processing

Volumn 21, Issue 8, 2012, Pages 3546-3559

  Liao, Shu ^a   Shen, Dinggang ^a  

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

Author keywords

Image guided radiation therapy; Learning based framework; Online update mechanism; Prostate localization

Indexed keywords

ANATOMICAL FEATURES; AUTOMATIC SEGMENTATIONS; COMPUTED TOMOGRAPHY; CT IMAGE; DATA SETS; FEATURE-BASED; IMAGE APPEARANCE; IMAGE CONTRASTS; IMAGE-GUIDED RADIATION THERAPY; INPUT IMAGE; LEARNING FRAMEWORKS; LEARNING-BASED FRAMEWORK; LOCALIZATION ACCURACY; MEDICAL IMAGE ANALYSIS; PROSTATE LOCALIZATION; SALIENT FEATURES; TREATMENT TIME;

COMPUTERIZED TOMOGRAPHY; IMAGE SEGMENTATION; RADIOTHERAPY; TISSUE;

UROLOGY;

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

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

EID: 84864142669     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2012.2194296     Document Type: Article

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