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.

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

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)