Image Processing

Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory

[+] Author Affiliations
Nishant Verma

University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas 78712, United States

St. David’s HealthCare, NeuroTexas Institute, Austin, Texas 78705, United States

Gautam S. Muralidhar

University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas 78712, United States

University of Texas MD Anderson Cancer Center, Department of Diagnostic Radiology, Houston, Texas 77030, United States

Alan C. Bovik

University of Texas at Austin, Department of Electrical and Computer Engineering, Austin, Texas 78712, United States

Matthew C. Cowperthwaite

St. David’s HealthCare, NeuroTexas Institute, Austin, Texas 78705, United States

Mark G. Burnett

St. David’s HealthCare, NeuroTexas Institute, Austin, Texas 78705, United States

Mia K. Markey

University of Texas at Austin, Department of Biomedical Engineering, Austin, Texas 78712, United States

University of Texas MD Anderson Cancer Center, Department of Imaging Physics, Houston, Texas 77030, United States

J. Med. Imag. 1(3), 034001 (Oct 01, 2014). doi:10.1117/1.JMI.1.3.034001
History: Received June 3, 2014; Revised August 21, 2014; Accepted September 10, 2014
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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.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Nishant Verma ; Gautam S. Muralidhar ; Alan C. Bovik ; Matthew C. Cowperthwaite ; Mark G. Burnett, et al.
"Three-dimensional brain magnetic resonance imaging segmentation via knowledge-driven decision theory", J. Med. Imag. 1(3), 034001 (Oct 01, 2014). ; http://dx.doi.org/10.1117/1.JMI.1.3.034001


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