Automatic segmentation of the liver using multi-planar anatomy and deformable surface model in abdominal contrast-enhanced CT images

Yujin Jang; Helen Hong; Jin Wook Chung; Young Ho Yoon

doi:10.1117/12.911691

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14 February 2012 Automatic segmentation of the liver using multi-planar anatomy and deformable surface model in abdominal contrast-enhanced CT images

Yujin Jang, Helen Hong, Jin Wook Chung, Young Ho Yoon

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Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 831436 (2012) https://doi.org/10.1117/12.911691
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

We propose an effective technique for the extraction of liver boundary based on multi-planar anatomy and deformable surface model in abdominal contrast-enhanced CT images. Our method is composed of four main steps. First, for extracting an optimal volume circumscribing a liver, lower and side boundaries are defined by positional information of pelvis and rib. An upper boundary is defined by separating the lungs and heart from CT images. Second, for extracting an initial liver volume, optimal liver volume is smoothed by anisotropic diffusion filtering and is segmented using adaptively selected threshold value. Third, for removing neighbor organs from initial liver volume, morphological opening and connected component labeling are applied to multiple planes. Finally, for refining the liver boundaries, deformable surface model is applied to a posterior liver surface and missing left robe in previous step. Then, probability summation map is generated by calculating regional information of the segmented liver in coronal plane, which is used for restoring the inaccurate liver boundaries. Experimental results show that our segmentation method can accurately extract liver boundaries without leakage to neighbor organs in spite of various liver shape and ambiguous boundary.

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Yujin Jang, Helen Hong, Jin Wook Chung, and Young Ho Yoon "Automatic segmentation of the liver using multi-planar anatomy and deformable surface model in abdominal contrast-enhanced CT images", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831436 (14 February 2012); https://doi.org/10.1117/12.911691

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