Multiple mean models of statistical shape and probability priors for automatic prostate segmentation

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

Volumn 6963 LNCS, Issue , 2011, Pages 35-46

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

^a UNIVERSITY OF GIRONA   (Spain)

^b UNIVERSITÉ DE BOURGOGNE   (France)

^c Girona Magnetic Resonance Center   (Spain)

^d HOSPITAL JOSEP TRUETA   (Spain)

Author keywords

Bayesian Framework; Multiple Statistical Shape; Posterior Probability Models; Prostate Segmentation

Indexed keywords

AUTOMATIC INITIALIZATION; BAYESIAN FRAMEWORKS; COMPUTER AIDED; DATA SETS; IMAGING ARTIFACTS; INTENSITY DISTRIBUTION; INTENSITY VARIATIONS; LOW CONTRAST; MEAN ABSOLUTE DISTANCE; MULTIPLE STATISTICAL SHAPE; P-VALUES; PARAMETRIC MODELS; POSTERIOR PROBABILITY; PROBABILISTIC FRAMEWORK; PROBABILISTIC INFORMATION; PROSTATE GLANDS; PROSTATE SEGMENTATION; SEGMENTATION ACCURACY; SEMI-AUTOMATIC SEGMENTATION; SHADOW REGIONS; SIMILARITY COEFFICIENTS; STATISTICAL MODELS; STATISTICAL SHAPES; TEXTURE MODELS; TRANSRECTAL ULTRASOUND IMAGES;

DISEASES; IMAGE ANALYSIS; MEDICAL IMAGING; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY DISTRIBUTIONS; STATISTICS; TEXTURES;

IMAGE SEGMENTATION;

EID: 80053502838     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23944-1_4     Document Type: Conference Paper

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