Liver Parenchyma Segmentation by FCM-Based Confidence Connected Region Growing Method

Abstract:

A FCM-based segmentation algorithm is proposed in this paper to improve the accuracy and efficiency of liver parenchyma segmentation. The proposed segmentation method consists of four steps as follows:First,we characterized the gray distribution of the unfiltered image. Second, combined with the Otsu algorithm and associated with a cropped liver image, we defined a gray interval as the livers intersity range. Third, The fuzzy c-means clustering algorithm was applied to define the confidence interval of traditional confidence connectivity method. Finally, we employed the improved confidence connected algorithm to extract the liver parenchyma from a large cross-section liver image. Experimental results show that the proposed segmentation method is feasible even for diseased liver images.

• Periodical: Applied Mechanics and Materials (Volumes 433-435)
• Pages: 348-352
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.433-435.348
• Online since: October 2013
• Authors: Yong Xiong Sun, Li Ping Huang*, Li Peng Liu, Tie Jun Guan, Qiu Yang Huang
• Keywords: Confidence Connected Region Growing, FCM, Liver Parenchyma Segmentation, Livers Intensity Range

References

[1] Yanda Chen, Susu Bao. Novel segmentation algorithm of region growing based on CT image sequences of liver[J]. Computer Engineering and Applications, 2010, 46(13): 188-190. (In Chinese).

[2] Hong Lan, Lu Zhang. Liver image segmentation algorithm based on the Snake model and optimized by watershed transformation[J]. Journal of Image and Graphics, 2012, 17(7): 873-879. (In Chinese).

[3] Huiyan Jiang, Ruijie Feng. Image segmentation method research based on improved variational level set and region growth[J]. Acta Electronica Sinica, 2012, 40(8): 1659: 1665. (In Chinese).

[4] Yan Gao, Boliang Wang. An improved region growing algorithm and its applications in kidney segmentation[J]. Journal of Xiamen University(natual science), 2012, 51(4): 702-704. (In Chinese).

[5] Fengping Peng, Susu Bao, Biqing Zeng. Segmentation of liver based on adaptive region growing[J]. Jisuanji Gongcheng yu Yingyong(Computer Engineering and Applications), 2010, 46(33): 198-200. (In Chinese).

[6] Foruzan A H, Zoroofi R A, Hori M, et al. Liver segmentation by intensity analysis and anatomical information in multi-slice CT images[J]. International journal of computer assisted radiology and surgery, 2009, 4(3): 287-297.

DOI: 10.1007/s11548-009-0293-2

[7] Foruzan A H, Zoroofi R A, Hori M, et al. A knowledge-based technique for liver segmentation in CT data[J]. Computerized Medical Imaging and Graphics, 2009, 33(8): 567-587.

DOI: 10.1016/j.compmedimag.2009.03.008

[8] Xiao Song, Ming Cheng, Boliang Wang, Shaohui Huang, Xiaoyang Huang. Confidence connected method for automatic liver segmentation[J]. Journal of Computer-Aided Design & Computer Graphics, 2012, 24（9）: 1188-1192. (In Chinese).