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Liver tumor segmentation based on level set
Author(s): Qianqian Pan; Liwei Zhang; Li Xia; Hongzhi Wang; Hai Li
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Paper Abstract

Liver tumor segmentation on computed tomography slices is a very difficult task because the medical images are often corrupted by noise and sampling artifacts. Besides, liver tumors are often surrounded by other abdominal structures with similar densities. Therefore, they often show the phenomenon of intensity inhomogeneity. These restrict the liver tumor segmentation. People tried to use traditional level set methods to segment the liver tumor, but the results were not satisfying due to the noise and the low gradient response on the liver tumor boundary. In this paper, we propose a multidistribution level set method which can overcome the insufficient segmentation and over-segmentation problems. We have done many experiments and compared our approach with the CV model and LSACM model. We also use the proposed method to segment the public data set from the “3D Liver Tumor Segmentation Challenge”. All results reveal that our method is better even for liver tumors with low contrast and blurred boundaries.

Paper Details

Date Published: 9 August 2018
PDF: 10 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108062N (9 August 2018); doi: 10.1117/12.2502810
Show Author Affiliations
Qianqian Pan, Hefei Institute of Physical Sciences (China)
Univ. of Science and Technology of China (China)
Liwei Zhang, Cancer Hospital, CAS (China)
Li Xia, Cancer Hospital, CAS (China)
Hongzhi Wang, Cancer Hospital, CAS (China)
Hai Li, Hefei Institute of Physical Sciences (China)
Cancer Hospital, CAS (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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