Confidence Connected Method for Automatic Liver Segmentation

Liver segmentation is the primary step for computer-aided liver disease diagnosis and surgery planning.In this paper,we present a fully automatic method for liver segmentation based on the confidence connected region growing.First,a modified curvature anisotropic diffusion filter is applied to CT images for noise reduction while preserving the liver structures,and then a series of seed points is selected automatically by intensity analysis.Then,the liver is segmented with a confidence connected region growing algorithm starting from the seed points.Finally,cavity filling method is used to improve the results of region growing.When tested on 10 abdominal CT image datasets,the average time for liver segmentation from one slice is 1.46s,and the average segmentation accuracy is 93.6%.Experimental results show that the proposed approach is accurate and efficient enough for the applications in clinical diagnosis and surgical navigation.