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Journal of Medical Imaging

Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory
Author(s): Nishant Verma; Gautam S. Muralidhar; Alan C. Bovik; Matthew C. Cowperthwaite; Mark G. Burnett; Mia K. Markey
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Paper Abstract

Brain tissue segmentation on magnetic resonance (MR) imaging is a difficult task because of significant intensity overlap between the tissue classes. We present a new knowledge-driven decision theory (KDT) approach that incorporates prior information of the relative extents of intensity overlap between tissue class pairs for volumetric MR tissue segmentation. The proposed approach better handles intensity overlap between tissues without explicitly employing methods for removal of MR image corruptions (such as bias field). Adaptive tissue class priors are employed that combine probabilistic atlas maps with spatial contextual information obtained from Markov random fields to guide tissue segmentation. The energy function is minimized using a variational level-set-based framework, which has shown great promise for MR image analysis. We evaluate the proposed method on two well-established real MR datasets with expert ground-truth segmentations and compare our approach against existing segmentation methods. KDT has low-computational complexity and shows better segmentation performance than other segmentation methods evaluated using these MR datasets.

Paper Details

Date Published: 1 October 2014
PDF: 15 pages
J. Med. Img. 1(3) 034001 doi: 10.1117/1.JMI.1.3.034001
Published in: Journal of Medical Imaging Volume 1, Issue 3
Show Author Affiliations
Nishant Verma, The Univ. of Texas at Austin (United States)
St. David’s HealthCare (United States)
Gautam S. Muralidhar, The Univ. of Texas at Austin (United States)
The Univ. of Texas MD Anderson Cancer Ctr. (United States)
Alan C. Bovik, The Univ. of Texas at Austin (United States)
Matthew C. Cowperthwaite, St. David's Health Care (United States)
Mark G. Burnett, St. David's Health Care (United States)
Mia K. Markey, The Univ. of Texas at Austin (United States)
The Univ. of Texas MD Anderson Cancer Ctr. (United States)


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