TITLE

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

AUTHOR(S)
Xin Liu; Haider, Masoom A.; Yetik, Imam Samil
PUB. DATE
January 2011
SOURCE
Journal of Electrical & Computer Engineering;2011, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
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
ACCESSION #
90416766

 

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