Optimal graph search based image segmentation for objects with complex topologies

Xiaomin Liu; Danny Z. Chen; Xiaodong Wu; Milan Sonka

doi:10.1117/12.811704

27 March 2009 Optimal graph search based image segmentation for objects with complex topologies

Xiaomin Liu, Danny Z. Chen, Xiaodong Wu, Milan Sonka

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 725915 (2009) https://doi.org/10.1117/12.811704
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

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).

Citation Download Citation

Xiaomin Liu, Danny Z. Chen, Xiaodong Wu, and Milan Sonka "Optimal graph search based image segmentation for objects with complex topologies", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 725915 (27 March 2009); https://doi.org/10.1117/12.811704

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