Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images

• Published in: IEEE transactions on image processing Vol. 24; no. 12; pp. 5315 - 5329
• Main Authors: Li, Guodong, Chen, Xinjian, Shi, Fei, Zhu, Weifang, Tian, Jie, Xiang, Dehui
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-12-2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Automation; Computed tomography; Deformable Graph Cut; Deformable models; Deformation; Euclidean distance; Euclidean Distance Transformation; Formability; Graphs; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Liver; Liver - diagnostic imaging; Liver Neoplasms - diagnostic imaging; Liver Segmentation; Mathematical models; Principal Component Analysis; Segmentation; Shape; Tomography, X-Ray Computed - methods; Liver segmentation; principal component analysis; deformable graph cut; euclidean distance transformation
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2015.2481326

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.