A supervised learning framework for automatic prostate segmentation in trans rectal ultrasound images

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

Volumn 7517 LNCS, Issue , 2012, Pages 190-200

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

^a UNIVERSITY OF GIRONA   (Spain)

^b UNIVERSITÉ DE BOURGOGNE   (France)

^c Girona Magnetic Resonance Imaging Center   (Spain)

^d HOSPITAL JOSEP TRUETA   (Spain)

Author keywords

Prostate Segmentation; Random Forest; Statistical Shape and Posterior Probability Models; Ultrasound

Indexed keywords

APPEARANCE MODELS; AUTOMATIC INITIALIZATION; COMPUTER AIDED; CONTRAST VARIATION; DATA SETS; IMAGING ARTIFACTS; INTENSITY DISTRIBUTION; INTENSITY VARIATIONS; MEAN ABSOLUTE DISTANCE; P-VALUES; PARAMETRIC REPRESENTATIONS; POSTERIOR PROBABILITY; PROBABILISTIC INFORMATION; PROSTATE GLANDS; PROSTATE SEGMENTATION; RANDOM FOREST CLASSIFICATION; RANDOM FORESTS; SEMI-AUTOMATIC SEGMENTATION; SHADOW REGIONS; SIMILARITY COEFFICIENTS; STATISTICAL MODELS; STATISTICAL SHAPES; TRAINING IMAGE; TRANSRECTAL ULTRASOUND IMAGES;

DECISION TREES; FACE RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY DISTRIBUTIONS; STATISTICS; SUPERVISED LEARNING; ULTRASONICS; UROLOGY;

IMAGE SEGMENTATION;

EID: 84866353882     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33140-4_17     Document Type: Conference Paper

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