CASIA OpenIR  > 中国科学院分子影像重点实验室
Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images
Li, Guodong1; Chen, Xinjian2; Shi, Fei2; Zhu, Weifang2; Tian, Jie1; Xiang, Dehui2
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2015-12-01
Volume24Issue:12Pages:5315-5329
SubtypeArticle
AbstractLiver segmentation is still a challenging task in medical image processing area due to the complexity of the liver's anatomy, low contrast with adjacent organs, and presence of pathologies. This investigation was used to develop and validate an automated method to segment livers in CT images. The proposed framework consists of three steps: 1) preprocessing; 2) initialization; and 3) segmentation. In the first step, a statistical shape model is constructed based on the principal component analysis and the input image is smoothed using curvature anisotropic diffusion filtering. In the second step, the mean shape model is moved using thresholding and Euclidean distance transformation to obtain a coarse position in a test image, and then the initial mesh is locally and iteratively deformed to the coarse boundary, which is constrained to stay close to a subspace of shapes describing the anatomical variability. Finally, in order to accurately detect the liver surface, deformable graph cut was proposed, which effectively integrates the properties and interrelationship of the input images and initialized surface. The proposed method was evaluated on 50 CT scan images, which are publicly available in two databases Sliver07 and 3Dircadb. The experimental results showed that the proposed method was effective and accurate for detection of the liver surface.
KeywordLiver Segmentation Principal Component Analysis Euclidean Distance Transformation Deformable Graph Cut
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2015.2481326
WOS KeywordMODEL ; TOMOGRAPHY ; LAPLACIAN
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000362488900016
Citation statistics
Cited Times:39[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10031
Collection中国科学院分子影像重点实验室
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Li, Guodong,Chen, Xinjian,Shi, Fei,et al. Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(12):5315-5329.
APA Li, Guodong,Chen, Xinjian,Shi, Fei,Zhu, Weifang,Tian, Jie,&Xiang, Dehui.(2015).Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(12),5315-5329.
MLA Li, Guodong,et al."Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.12(2015):5315-5329.
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