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Proceedings Paper

Optimal graph search based image segmentation for objects with complex topologies
Author(s): Xiaomin Liu; Danny Z. Chen; Xiaodong Wu; Milan Sonka
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Paper Abstract

Segmenting objects with complicated topologies in 3D images is a challenging problem in medical image processing, especially for objects with multiple interrelated surfaces. In this paper, we extend a graph search based technique to simultaneously identifying multiple interrelated surfaces for objects that have complex topologies (e.g., with tree-like structures) in 3D. We first perform a pre-segmentation on the input image to obtain basic information of the objects' topologies. Based on the initial pre-segmentation, the original image is resampled along judiciously determined directions to produce a set of vectors of voxels (called voxel columns). The resampling process utilizes medial axes to ensure that voxel columns of appropriate lengths are used to capture the sought object surfaces. Then a geometric graph is constructed whose edges connect voxels in the resampled voxel columns and enforce the smoothness constraint and separation constraint on the sought surfaces. Validation of our algorithm was performed on the segmentation of airway trees and lung vascular trees in human in-vivo CT scans. Cost functions with directional information are applied to distinguish the airway inner wall and outer wall. We succeed in extracting the outer airway wall and optimizing the location of the inner wall in all cases, while the vascular trees are optimized as well. Comparing with the pre-segmentation results, our approach captures the wall surfaces more accurately, especially across bifurcations. The statistical evaluation on a double wall phantom derived from in-vivo CT images yields highly accurate results of the wall thickness measurement on the whole tree (with mean unsigned error 0.16 ± 0.16mm).

Paper Details

Date Published: 27 March 2009
PDF: 10 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725915 (27 March 2009); doi: 10.1117/12.811704
Show Author Affiliations
Xiaomin Liu, Univ. of Notre Dame (United States)
Danny Z. Chen, Univ. of Notre Dame (United States)
Xiaodong Wu, 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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