Segmentation Method for Liver Organ Based on Image Sequence Context_Journal of Biomedical Engineering

Authors：

 ZHANGMeiyun , FANGBin , WANGYi , ZHONGNanchang

College of Computer Science, Chongqing University, Chongqing 400044, China;

Corresponding author：

ZHANGMeiyun, Email: outwindows@126.com

Keywords：

context; image sequence; liver segmentation; level set; region growing

DOI：

10.7507/1001-5515.20150199

Video：

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Abstract Full text Figures/Tables Video References Cited by

In view of the problems of more artificial interventions and segmentation defects in existing two-dimensional segmentation methods and abnormal liver segmentation errors in three-dimensional segmentation methods, this paper presents a semi-automatic liver organ segmentation method based on the image sequence context. The method takes advantage of the existing similarity between the image sequence contexts of the prior knowledge of liver organs, and combines region growing and level set method to carry out semi-automatic segmentation of livers, along with the aid of a small amount of manual intervention to deal with liver mutation situations. The experiment results showed that the liver segmentation algorithm presented in this paper had a high precision, and a good segmentation effect on livers which have greater variability, and can meet clinical application demands quite well.

Citation： ZHANGMeiyun, FANGBin, WANGYi, ZHONGNanchang. Segmentation Method for Liver Organ Based on Image Sequence Context. Journal of Biomedical Engineering, 2015, 32(5): 1125-1130. doi: 10.7507/1001-5515.20150199 Copy

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1. LIM S J, JEONG Y Y, Ho Y S. Automatic liver segmentation for volume measurement in CT Images[J]. Journal of Visual Communication and Image Representation, 2006, 17(4):860-875.
2. WAN S Y, HIGGINS W E. Symmetric region growing[J]. IEEE Trans on Image Processing, 2003, 12(9):1007-1015.
3. MENDOZA C S, ACHA B, Serrano C, et al. Fast parameter-free region growing segmentation with application to surgical planning[J]. Springer Machine Vision and Applications, 2012, 23(1):165-177.
4. GAMIO J C, BELONGIE S J. MAJUMDAR S. Normalized Cuts in 3-D for Spinal MRI Segmentation[J]. IEEE Transactions on Medical Imaging, 2004, 23(1):36-44.
5. COMANICIU D, MEER P. Mean Shift:A robust approach toward feature space analysis[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002, 24(5):603-619.
6. 全刚.基于活动轮廓的图像分割方法研究[D].国防科学技术大学,2010.
7. 王斌,李洁,高新波.一种基于边缘与区域信息的先验水平集图像分割方法[J].计算机学报,2012,35(5):1067-1072.
8. LI C, XU C, GUI C, et al. Distance regularized level set evolution and its application to image segmentation[J]. IEEE Trans on Image Processing, 2010, 19(12):3243-3254.
9. FREEDMAN D, ZHANG T. Interactive graph cut based segmentation with shape priors[C]//Conference on Computer Vision and Pattern Recognition. San Diego. 2005:755-762.
10. MASSOPTIER L, CASCIARO S. Fully automatic liver segmentation through graph-cut technique[C]//Conference on the IEEE Engineering in Medicine and Biology Society. Lyon. 2007:5243-5246.
11. RUSKO L, BEKES G, NEMETH G, et al. Fully automatic liver segmentation for contrast-enhanced CT images[J]. MICCAI Workshop. 3D Segmentation in the Clinic:A Grand Challenge, 2007, 2(7):143-150.
12. MASSOPTIER L, CASCIARO S. A new fully automatic and robust algorithm for fast segmentation of liver tissue and tumors from CT scans[J]. European Radiology, 2008, 18(8):1658-1665.

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