Paper
27 March 2009 Globally optimal 3D graph search incorporating both edge and regional information: application to aortic MR image segmentation
Author Affiliations +
Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725913 (2009) https://doi.org/10.1117/12.812040
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
We present a novel method for incorporating both edge and regional image information in a 3-D graph-theoretic approach for globally optimal surface segmentation. The energy functional takes a ratio form of the "onsurface" cost and the "in-region" cost. We thus introduce an optimal surface segmentation model allowing regional information such as volume, homogeneity and texture to be included with boundary information such as intensity gradients. Compared to the linear combination as in the standard active contour energies, this ratioform energy is parameter free with no bias toward either a large or small region. Our method is the first attempt to use a ratio-form energy functional in graph search framework for high dimensional image segmentation, which delivers a globally optimal solution in polynomial time. The globally optimal surface can be achieved by solving a parametric maximum flow problem in the time complexity of computing a single maximum flow. Our new approach is applied to the aorta segmentation of 15 3-D MR aortic images from 15 subjects. Compared to an expert-defined independent standard, the overall mean unsigned surface positioning error was 0.76± 0.88 voxels. Our experiments showed that the incorporation of the regional information was effective to alleviate the interference of adjacent objects.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qi Song, Xiaodong Wu, Xin Dou, and Milan Sonka "Globally optimal 3D graph search incorporating both edge and regional information: application to aortic MR image segmentation", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725913 (27 March 2009); https://doi.org/10.1117/12.812040
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Cited by 1 scholarly publication.
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KEYWORDS
Image segmentation

3D image processing

Magnetic resonance imaging

Image processing algorithms and systems

3D modeling

Detection and tracking algorithms

Medical imaging

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