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Proceedings Paper

Segmentation of liver and liver tumor for the Liver-Workbench
Author(s): Jiayin Zhou; Feng Ding; Wei Xiong; Weimin Huang; Qi Tian; Zhimin Wang; Sudhakar K. Venkatesh; Wee Kheng Leow
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Paper Abstract

Robust and efficient segmentation tools are important for the quantification of 3D liver and liver tumor volumes which can greatly help clinicians in clinical decision-making and treatment planning. A two-module image analysis procedure which integrates two novel semi-automatic algorithms has been developed to segment 3D liver and liver tumors from multi-detector computed tomography (MDCT) images. The first module is to segment the liver volume using a flippingfree mesh deformation model. In each iteration, before mesh deformation, the algorithm detects and avoids possible flippings which will cause the self-intersection of the mesh and then the undesired segmentation results. After flipping avoidance, Laplacian mesh deformation is performed with various constraints in geometry and shape smoothness. In the second module, the segmented liver volume is used as the ROI and liver tumors are segmented by using support vector machines (SVMs)-based voxel classification and propagational learning. First a SVM classifier was trained to extract tumor region from one single 2D slice in the intermediate part of a tumor by voxel classification. Then the extracted tumor contour, after some morphological operations, was projected to its neighboring slices for automated sampling, learning and further voxel classification in neighboring slices. This propagation procedure continued till all tumorcontaining slices were processed. The performance of the whole procedure was tested using 20 MDCT data sets and the results were promising: Nineteen liver volumes were successfully segmented out, with the mean relative absolute volume difference (RAVD), volume overlap error (VOE) and average symmetric surface distance (ASSD) to reference segmentation of 7.1%, 12.3% and 2.5 mm, respectively. For live tumors segmentation, the median RAVD, VOE and ASSD were 7.3%, 18.4%, 1.7 mm, respectively.

Paper Details

Date Published: 11 March 2011
PDF: 9 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 79622I (11 March 2011); doi: 10.1117/12.877927
Show Author Affiliations
Jiayin Zhou, A*STAR Institute for Infocomm Research (Singapore)
Feng Ding, National Univ. of Singapore (Singapore)
Wei Xiong, National Univ. of Singapore (Singapore)
Weimin Huang, A*STAR Institute for Infocomm Research (Singapore)
Qi Tian, A*STAR Institute for Infocomm Research (Singapore)
Zhimin Wang, A*STAR Institute for Infocomm Research (Singapore)
Sudhakar K. Venkatesh, National Univ. of Singapore (Singapore)
Wee Kheng Leow, National Univ. of Singapore (Singapore)


Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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