DocumentCode :
3606746
Title :
Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images
Author :
Guodong Li ; Xinjian Chen ; Fei Shi ; Weifang Zhu ; Jie Tian ; Dehui Xiang
Author_Institution :
Inst. of Autom., Beijing, China
Volume :
24
Issue :
12
fYear :
2015
Firstpage :
5315
Lastpage :
5329
Abstract :
Liver 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 inter-relationship 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.
Keywords :
computerised tomography; graph theory; image segmentation; iterative methods; medical image processing; 3Dircadb database; CT images; Euclidean distance transformation; Sliver07 database; automatic liver segmentation; curvature anisotropic diffusion filtering; deformable graph cut; iterative method; medical image processing; principal component analysis; shape constraints; statistical shape model; Adaptation models; Computed tomography; Deformable models; Euclidean distance; Image segmentation; Liver; Shape; Deformable Graph Cut; Euclidean Distance Transformation; Liver Segmentation; Liver segmentation; Principal Component Analysis; deformable graph cut; euclidean distance transformation; principal component analysis;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2015.2481326
Filename :
7274362
Link To Document :
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