A compact method for prostate zonal segmentation on multiparametric MRIs
2014
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.
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