A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance

Proceedings - International Conference on Image Processing, ICIP

Volumn , Issue , 2011, Pages 713-716

  Ghose, S ^a,c   Oliver, A ^a   Marti, R ^a   Llado X ^a   Freixenet, J ^a   Vilanova, J C ^b   Meriaudeau, F ^c  

^a UNIVERSITY OF GIRONA   (Spain)

^b Girona Magnetic Resonance Center   (Spain)

^c UNIVERSITÉ DE BOURGOGNE   (France)

Author keywords

Active Appearance Model; Bayes Classification; Expectation Maximization; Prostate Segmentation

Indexed keywords

ACTIVE APPEARANCE MODELS; AUTOMATIC SEGMENTATIONS; BAYES CLASSIFICATION; COMPUTATIONALLY EFFICIENT; DATA SETS; EXPECTATION MAXIMIZATION; IMAGING ARTIFACTS; INTENSITY DISTRIBUTION; INTENSITY HETEROGENEITY; MEAN ABSOLUTE DISTANCE; MICROCALCIFICATIONS; PARAMETRIC MODELS; POSTERIOR PROBABILITY; PROBABILISTIC FRAMEWORK; PROSTATE CANCERS; PROSTATE DISEASE; PROSTATE SEGMENTATION; PROSTATE VOLUME; SIMILARITY COEFFICIENTS; SPECKLE NOISE; STATISTICAL MODELS; TRANSRECTAL ULTRASOUND IMAGES; TRUS IMAGES;

BIOMINERALIZATION; DIAGNOSIS; DISEASES; FACE RECOGNITION; PRINCIPAL COMPONENT ANALYSIS;

IMAGE SEGMENTATION;

EID: 84856306185     PISSN: 15224880     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIP.2011.6116653     Document Type: Conference Paper

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