Automatic Segmentation of Liver Tumor in CT Volumes Using Nonlinear Enhancement and Graph Cuts

被引：0
|
作者：
Liao M. [1 ,2 ]
Liu Y. [1 ]
Ouyang J. [1 ]
Yu J. [1 ]
Zhao Y. [2 ]
Zhang B. [2 ]
机构：
[1] School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan
[2] School of Automation, Central South University, Changsha
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 06期
关键词：
Graph cuts; Liver tumor; Medical image segmentation; Nonlinear enhancement;
D O I：
10.3724/SP.J.1089.2019.17258
中图分类号：
学科分类号：
摘要：
Aiming at the segmentation challenges caused by low contrast, fuzzy boundary and variant grayscale of liver tumors in abdominal CT images, an automatic liver tumor segmentation method based on nonlinear enhancement and graph cuts is proposed. Firstly, adaptive piecewise nonlinear enhancement and iterative convolution operation are used to improve the contrast of healthy liver parenchyma and tumors according to the gray-level distribution characteristics of liver region. Then, the enhancement result and image edge information are effectively integrated into graph cuts cost computation to segment the liver tumors initially and automatically. Finally, three-dimensional morphological opening operation is performed on the initial segmentation result to remove segmentation errors and increase accuracy. The experimental results on 3Dircadb and XYH databases show that the proposed method can segment liver tumors from abdominal CT volumes effectively and automatically, and the comprehensive segmentation performance of the proposed method is superior to that of several existing methods. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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页码：1030 / 1038
页数：8
相关论文
共 16 条
  • [1] Hong Y.F., Chen Z.H., Ma X.K., Et al., Comparison of five models for end-stage liver disease in predicting the survival rate of patients with advanced hepatocellular carcinoma, Tumour Biology, 37, 4, pp. 5265-5273, (2016)
  • [2] Conze P.H., Noblet V., Rousseau F., Et al., Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans, International Journal of Computer Assisted Radiology and Surgery, 12, 2, pp. 223-233, (2017)
  • [3] Rajagopal R., Subbaiah P., A survey on liver tumor detection and segmentation methods, ARPN Journal of Engineering and Applied Sciences, 10, 6, pp. 2681-2685, (2015)
  • [4] Foruzan A.H., Chen Y.W., Improved segmentation of low-con trast lesions using sigmoid edge model, International Journal of Computer Assisted Radiology and Surgery, 11, 7, pp. 1267-1283, (2016)
  • [5] Wu W.W., Wu S.C., Zhou Z.H., Et al., 3D liver tumor segmentation in CT images using improved fuzzy C-means and graph cuts, BioMed Research International, 2017, (2017)
  • [6] Hame Y., Pollari M., Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation, Medical Image Analysis, 16, 1, pp. 140-149, (2012)
  • [7] Li B.N., Chui C.K., Chang S., Et al., A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images, Expert Systems with Applications, 39, 10, pp. 9661-9668, (2012)
  • [8] Kadoury S., Vorontsov E., Tang A., Metastatic liver tumour segmentation from discriminant Grassmannian manifolds, Physics in Medicine and Biology, 60, 16, pp. 6459-6478, (2015)
  • [9] Huang W., Li N., Lin Z., Et al., Liver tumor detection and segmentation using kernel-based extreme learning machine, Proceedings of the 35th Annual International Conference of IEEE Engineering in Medicine and Biology Society, pp. 3662-3665, (2013)
  • [10] Moghbel M., Mashohor S., Mahmud R., Et al., Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring, EXCLI Journal, 15, pp. 406-423, (2016)
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