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Proceedings Paper

A compact method for prostate zonal segmentation on multiparametric MRIs
Author(s): Y. Chi; H. Ho; Y. M. Law; Q. Tian; H. J. Chen; K. J. Tay; J. Liu
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Paper Abstract

Automatic segmentation of the prostate zones has great potential of improving the accuracy of lesion detection during the image-guided prostate interventions. In this paper, we present a novel compact method to segment the prostate and its zones using multi-parametric magnetic resonance imaging (MRI) and the anatomical priors. The proposed method comprises of a prostate tissue representation using Gaussian mixture model (GMM), a prostate localization using the mean shift with the kernel of the prostate atlas and a prostate partition using the probabilistic valley between zones. The proposed method was tested on four sets of multi-parametric MRIs. The average Dice coefficient resulted from the segmentation of the prostate is 0.80 ± 0.03, the central zone 0.83 ± 0.04, and the peripheral zone 0.52 ± 0.09. The average computing time of the online segmentation is 1 min and 10 s per datasets on a PC with 2.4 GHz and 4.0 GB RAM. The proposed method is fast and has the potential to be used in clinical practices.

Paper Details

Date Published: 12 March 2014
PDF: 9 pages
Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90360N (12 March 2014); doi: 10.1117/12.2043334
Show Author Affiliations
Y. Chi, Singapore Bioimaging Consortium (Singapore)
H. Ho, Singapore General Hospital (Singapore)
Y. M. Law, Singapore General Hospital (Singapore)
Q. Tian, Singapore Bioimaging Consortium (Singapore)
H. J. Chen, Biobot Surgical Co. (Singapore)
K. J. Tay, Singapore General Hospital (Singapore)
J. Liu, Singapore Bioimaging Consortium (Singapore)


Published in SPIE Proceedings Vol. 9036:
Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling
Ziv R. Yaniv; David R. Holmes III, Editor(s)

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