Liver Segmentation from Abdominal CT Volumes Based on Graph Cuts and Border Marching
LIAO Miao① ZHAO Yuqian①② ZENG Yezhan① HUANG Zhongchao① ZOU Beiji②
①(School of Geosciences and Info-Physics, Central South University, Changsha 410083, China) ②(School of Information Science and Engineering, Central South University, Changsha 410083, China)
A novel method for liver segmentation from abdominal CT volumes based on graph cuts and border marching is proposed. First, to exclude complex background and highlight liver region, liver intensity and appearance models are built according to the characteristics of a given CT volume. Then, the intensity and appearance models together with location information from neighbor segmented slice are effectively integrated into graph cuts cost computation to segment the CT volume initially and automatically. Finally, to solve the under-segmentation issue of liver vessel, a boundary compensation method based on border marching is proposed. The proposed method is tested and compared with some other methods on 30 CT volumes from XHCSU14 and SLIVER07 databases. The experimental results show that the proposed method can segment livers integrally and effectively from abdominal CT volumes, with higher accuracy and robustness.
SELVER M A, KOCAOGLU A, DEMIR G K, et al. Patient oriented and robust automatic liver segmentation for pre-evaluation of liver transplantation[J]. Computers in Biology and Medicine, 2008, 38(7): 765-784. doi: 10.1016/j. compbiomed.2008.04.006.
[2]
LU X Q, WU J S, REN X Y, et al. The study and application of the improved region growing algorithm for liver segmentation[J]. Optik, 2014, 125(9): 2142-2147. doi: 10. 1016/j.ijleo.2013.10.049.
ZHANG Xiaoqiang, XIONG Boli, and KUANG Gangyao. A ship target discrimination method based on change detection in SAR imagery[J]. Journal of Electronics & Information Technology, 2015, 37(1): 63-70. doi: 10.11999/JEIT140143.
HAN Ming, LIU Jiaomin, MENG Junying, et al. Local energy information combined with improved signed distance regularization term for image target segmentation algorithm[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2047-2054. doi: 10.11999/ JEIT141473.
[5]
PENG J L, WANG Y, and KONG D X. Liver segmentation with constrained convex variational model[J]. Pattern Recognition Letters, 2014, 43(1): 81-88. doi: 10.1016/j.patrec. 2013.07.010.
[6]
AFIFI A and NAKAGUCHI T. Liver segmentation approach using graph cuts and iteratively estimated shape and intensity constrains[C]. Medical Image Computing and Computer-Assisted Intervention, Nice, 2012, 7511: 395-403.
[7]
CHEN X, UDUPA J K, BAGCI U, et al. Medical image segmentation by combining graph cuts and oriented active appearance models[J]. IEEE Transactions on Image Processing, 2012, 21(4): 2035-2046. doi: 10.1109/TIP.2012. 2186306.
[8]
HEIMANN T, MEINZER H, and WOLF I. A statistical deformable model for the segmentation of liver CT volumes[C]. MICCAI Workshop 3-D Segmentation Clinic Grand Challenge, Brisbane, 2007: 161-166. doi: 10.1109/ IEMBS.2010.5626470.
[9]
KAINMULLER D, LANGE T, and LAMECKER H. Shape constrained automatic segmentation of the liver based on a heuristic intensity model[C]. MICCAI Workshop 3-D Segmentation Clinic Grand Challenge, Brisbane, 2007: 109-116.
[10]
LIAO Miao, ZHAO Yuqian, LI Xianghua, et al. Automatic segmentation for cell images based on bottleneck detection and ellipse fitting[J]. Neurocomputing, 2015, 173(3): 615-622. doi: 10.1016/j.neucom.2015.08.006.
[11]
HEIMANN T, GINNEKEN B V, and STYNER M A. Segmentation of the liver 2007 (SLIVER07)[OL]. http:// sliver07.isi.uu.nl/, 2007.
[12]
HEIMANN T, GINNEKEN B V, STYNER M A, et al. Comparison and evaluation of methods for liver segmentation from CT datasets[J]. IEEE Transactions on Medical Imaging, 2009, 28(8): 1251-1265. doi: 10.1109/TMI.2009.2013851.