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Proceedings Paper

Liver vessel tree segmentation based on a hybrid graph cut / fuzzy connectedness method
Author(s): Xinjian Chen
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Paper Abstract

In the monitoring of oncological therapy, the prediction of liver tumor growth from consecutive CT scans is an important aspect in deciding the treatment planning. The accurate segmentation of liver vessel tree is fundamental for successful prediction of the tumor growth. In this paper, we report a 3D liver vessel tree segmentation method based on the hybrid graph cut (GC) / fuzzy connectedness (FC) method. GC is a popular image segmentation technique. However, it is not always efficient when segmenting thin elongated objects due to its "shrinking bias". To overcome this problem, we propose to impose an additional connectivity prior, which comes from the FC segmentation results. The proposed method synergistically combines the GC with FC methods. The proposed method consists of two main steps. First, the FC method is applied to initially segment the liver vessel tree, which provided the connectivity prior to the subsequent GC method. Second, the connectivity prior integrated GC method is employed to refine the segmented liver vessel tree. The proposed method was tested on 10 clinical portal venous phase CT data sets. The preliminary results showed the feasibility and efficiency of the proposed method. The accuracy of segmentation on this dataset, expressed in sensitivity, was 60%, 92% and 100% for vessel diameters in the range of 0.5 to 1, 1 to 2 and >2 mm, respectively.

Paper Details

Date Published: 14 February 2012
PDF: 7 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141K (14 February 2012); doi: 10.1117/12.911591
Show Author Affiliations
Xinjian Chen, The Univ. of Iowa (United States)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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