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Proceedings Paper

Globally optimal 3D graph search incorporating both edge and regional information: application to aortic MR image segmentation
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Paper 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.

Paper Details

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725913 (27 March 2009); doi: 10.1117/12.812040
Show Author Affiliations
Qi Song, The Univ. of Iowa (United States)
Xiaodong Wu, The Univ. of Iowa (United States)
Xin Dou, The Univ. of Iowa (United States)
Milan Sonka, The Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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