title: Efficient liver segmentation in CT images based on graph cuts and bottleneck detection.
abstract: Liver segmentation from abdominal computed tomography (CT) volumes is extremely important for computer-aided liver disease diagnosis and surgical planning of liver transplantation. Due to ambiguous edges, tissue adhesion, and variation in liver intensity and shape across patients, accurate liver segmentation is a challenging task. In this paper, we present an efficient semi-automatic method using intensity, local context, and spatial correlation of adjacent slices for the segmentation of healthy liver regions in CT volumes. An intensity model is combined with a principal component analysis (PCA) based appearance model to exclude complex background and highlight liver region. They are then integrated with location information from neighboring slices into graph cuts to segment the liver in each slice automatically. Finally, a boundary refinement method based on bottleneck detection is used to increase the segmentation accuracy. Our method does not require heavy training process or statistical model construction, and is capable of dealing with complicated shape and intensity variations. We apply the proposed method on XHCSU14 and SLIVER07 databases, and evaluate it by MICCAI criteria and Dice similarity coefficient. Experimental results show our method outperforms several existing methods on liver segmentation.
authors: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; School of Information Science and Engineering, Central South University, Changsha 410083, China.
authors: Miao
authors: M
authors: Liao
authors: School of Information Science and Engineering, Central South University, Changsha 410083, China. Electronic address: zyq@csu.edu.cn.
authors: Yu-Qian
authors: YQ
authors: Zhao
authors: The Third Xiangya Hospital, Central South University, Changsha 410083, China. Electronic address: cjr.wangwei@vip.163.com.
authors: Wei
authors: W
authors: Wang
authors: School of Information Science and Engineering, Central South University, Changsha 410083, China.
authors: Ye-Zhan
authors: YZ
authors: Zeng
authors: School of Information Science and Engineering, Central South University, Changsha 410083, China.
authors: Qing
authors: Q
authors: Yang
authors: College of Computing Sciences, New Jersey Institute of Technology, Newark, NJ 07102, USA.
authors: Frank Y
authors: FY
authors: Shih
authors: School of Information Science and Engineering, Central South University, Changsha 410083, China.
authors: Bei-Ji
authors: BJ
authors: Zou
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed
keywords: Bottleneck detection; Gaussian fitting; Graph cuts; Liver segmentation; PCA