Liver Segmentation from CT Image Using Fuzzy Clustering and Level Set

Xuechen Li, Suhuai Luo, Jiaming Li

The CSIRO ICT Centre, Australia.
The University of Newcastle, Australia.

DOI: 10.4236/jsip.2013.43B007                

Abstract

This paper presents a fully automatic segmentation method of liver CT scans using fuzzy c-mean clustering and level set. First, the contrast of original image is enhanced to make boundaries clearer; second, a spatial fuzzy c-mean clustering combining with anatomical prior knowledge is employed to extract liver region automatically; thirdly, a distance regularized level set is used for refinement; finally, morphological operations are used as post-processing. The experiment result shows that the method can achieve high accuracy (0.9986) and specificity (0.9989). Comparing with standard level set method, our method is more effective in dealing with over-segmentation problem.

Keywords

Liver Segmentation; Fuzzy c-Mean Clustering; Level Set

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	L. Ruskó, G. Bekes, G. Németh and M. Fidrich, “Fully Automatic Liver Segmentation for Contrast Enhanced CT Images,” MICCAI Wshp. 3D Segmentation in the Clinic: A Grand Challenge, 2007, pp. 143-150.
[2]	S. S. Kumar, R. S. Moni and J. Rajeesh, “Automatic Liver and Lesion Segmentation: A Primary Step in Diagnosis of Liver Diseases,” VSignal, Image and Video Processing, 2011.
[3]	J. B. Huang, L. Q. Meng, W. H. Qu and C. H. Wang, “Based on Statistical Analysis and 3D Region Growing Segmentation Method of Liver,” Advanced Computer Control (ICACC), 2011, pp. 478-482.
[4]	C. M. Li, C. Y. Xu, C. F. Gui and M. D. Fox, “Distance Regularized Level Set Evolution and Its Application to Image Segmentation,” IEEE Transactions onImage Processing, Vol. 19, No. 12, 2010, pp. 3243-3254.
[5]	B. N. Li, C. K. Chui, S. Chang and S. H. Ong, “Integrating Spatial Fuzzy Clustering with Level Set Methods for Automated Medical Image Segmentation,” Computers in Biology and Medicine, Vol. 41, No. 1, 2011, pp. 1–10. doi:10.1016/j.compbiomed.2010.10.007
[6]	C. Platero, M. C. Tobar, J. Sanguino, J. M. Poncela and O. Velasco, “Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors,” Pattern Recognition and Image Analysis, Vol. 6669,2011, pp. 109-116. doi:10.1007/978-3-642-21257-4_14
[7]	Y. Q. Zhao, Y. L. Zan, X. F. Wang and G. Y. Li, “Fuzzy C-means Clustering-Based Multilayer Perception Neural Network for Liver CT Images Automatic Segmentation,” Control and Decision Conference (CCDC), Xuzhou, May 2010, pp. 3423-3427.
[8]	K. S. Chuang, H. L. T. zeng, S. Chen, J. Wu and J. Chen, “Fuzzy C-means Image Segmentation with Weighted Membership Functions with Spatial Constraints,” Computerized Medical Imaging and Graphics, Vol. 30, No. 1,2006, pp. 9–15. doi:10.1016/j.compmedimag.2005.10.001
[9]	X. Zhang, J. Tian, K. X. Deng, Y. F. Wu and X. L. Li: “Automatic Liver Segmentation Using a Statistical Shape Model with Optimal Surface Detection,” IEEE Transactions on Biomedical Engineering, Vol. 57, No. 10, 2010, pp. 2622-2626. doi.org/10.1109/TBME.2010.2056369
[10]	J. Lu, D. F. Wang, L. Shi and A. Heng, “Automatic Liver Segmentation in CT Images Based on Support Vector Machine,” Biomedical and Health Informatics (BHI), 2012, pp. 333-336.
[11]	S. Luo, Q. Hu, X. He, J. Li, J. Jin and M. Park, “Automatic Liver Parenchyma Segmentation from Abdominal CT Images Using Support Vector Machines,” Proceedings of 2009 Icme International Conference on Complex Medical Engineering, Tempe, 9-11 April 2009, pp. 1-5.
[12]	X. Zhang, J. Tian, D. H. Xiang, X. L. Li and K. X. Deng, “Interactive liver tumor segmentation from CT scans using support vector classification with watershed,” Engineering in Medicine and Biology Society, EMBC, 2011, pp. 6005-6008.