A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images

Medical Image Analysis

Volumn 17, Issue 6, 2013, Pages 587-600

  Ghose, Soumya ^a   Oliver, Arnau ^c   Mitra, Jhimli ^a   Martí, Robert ^c   Lladó, Xavier ^c   Freixenet, Jordi ^c   Sidibé, Désiré ^b   Vilanova, Joan C ^d   Comet, Josep ^e   Meriaudeau, Fabrice ^b  

^a CSIRO   (Australia)

^b UNIVERSITÉ DE BOURGOGNE   (France)

^c UNIVERSITY OF GIRONA   (Spain)

^d Girona Magnetic Resonance Center   (Spain)

^e HOSPITAL JOSEP TRUETA   (Spain)

Author keywords

Prostate segmentation; Random forest classification; Spectral clustering; Statistical shape and appearance model

Indexed keywords

INTRA-OBSERVER VARIABILITY; MULTIMODAL IMAGE REGISTRATION; PROSTATE SEGMENTATION; RANDOM FOREST CLASSIFICATION; SEGMENTATION ACCURACY; SIMILARITY COEFFICIENTS; SPECTRAL CLUSTERING; STATISTICAL SHAPES;

DECISION TREES; FACE RECOGNITION; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; ULTRASONICS; UROLOGY;

IMAGE SEGMENTATION;

ACCURACY; ARTICLE; HUMAN; IMAGE ANALYSIS; MALE; MODEL; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; PROBABILITY; PROSTATE SEGMENTATION; PROSTATE VOLUME; RANDOM FOREST; ULTRASOUND; VALIDATION PROCESS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATE; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; ULTRASONOGRAPHY;

EID: 84877798775     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2013.04.001     Document Type: Article

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