Paper Info
Title
Toward automatic zonal segmentation of prostate by combining a deformable model and a probabilistic framework
Abstract
This paper introduces an original method for automatic 3D segmentation of the prostate gland from Magnetic Resonance Imaging data. A statistical geometric model is used as a priori knowledge. Prostate boundaries are then optimized by a Bayesian classification based on Markov fields modelling. We compared the accuracy of this algorithm, free from any manual correction, with contours outlined by an expert radiologist. In 3 random cases, including prostates with cancer and benign prostatic hypertrophy (BPH), mean Hausdorff's distance (HD) and Overlap Ration (OR) were 8.07 mm and 0.82, respectively. Despite fast computing times, this new method showed satisfying results, even at prostate base and apex. Also, we believe that this approach may allow delineating the peripheral zone (PZ) and the transition zone (TZ) within the gland in a near future.
Year
DOI
Venue
2008
10.1109/ISBI.2008.4540934
ISBI
Keywords
Field
DocType
Markov processes,belief networks,biological organs,biomedical MRI,cancer,image segmentation,medical image processing,probabilistic logic,Bayesian classification,Markov field modelling,apex,automatic 3D zonal segmentation,benign prostatic hypertrophy,cancer,deformable model,magnetic resonance Imaging data,mean Hausdorff s distance,overlap ration,peripheral zone,probabilistic framework,prostate gland,statistical geometric model,transition zone,3D deformable model,Markov fields,Prostate cancer,segmentation
Computer vision,Pattern recognition,Naive Bayes classifier,Segmentation,Computer science,A priori and a posteriori,Markov chain,Geometric modeling,Image segmentation,Artificial intelligence,Prostate,Probabilistic logic
Conference
ISSN
Citations 
PageRank 
1945-7928
1
0.63
References 
Authors
3
6
Name
Order
Citations
PageRank
Nasr Makni1515.85
Philippe Puech2454.56
Renaud Lopes3212.45
Anne-sophie Dewalle4263.53
Olivier Colot512915.55
Nacim Betrouni66711.63