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Journal of Electrical and Computer Engineering
Volume 2011, Article ID 410912, 11 pages
Research Article

Unsupervised 3D Prostate Segmentation Based on Diffusion-Weighted Imaging MRI Using Active Contour Models with a Shape Prior

1Department of Electrical and Computer Engineering, Medical Imaging Research Center (MIRC), Illinois Institute of Technology, Chicago, IL 60616, USA
2Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital, Toronto, ON, Canada M5G 1X6

Received 29 March 2011; Revised 10 June 2011; Accepted 24 June 2011

Academic Editor: Tamal Bose

Copyright © 2011 Xin Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Accurate estimation of the prostate location and volume from in vivo images plays a crucial role in various clinical applications. Recently, magnetic resonance imaging (MRI) is proposed as a promising modality to detect and monitor prostate-related diseases. In this paper, we propose an unsupervised algorithm to segment prostate with 3D apparent diffusion coefficient (ADC) images derived from diffusion-weighted imaging (DWI) MRI without the need of a training dataset, whereas previous methods for this purpose require training datasets. We first apply a coarse segmentation to extract the shape information. Then, the shape prior is incorporated into the active contour model. Finally, morphological operations are applied to refine the segmentation results. We apply our method to an MR dataset obtained from three patients and provide segmentation results obtained by our method and an expert. Our experimental results show that the performance of the proposed method is quite successful.