Efficient Johnson-S(B) Mixture Model for Segmentation of CT Liver Image

To overcome the problem that the traditional Gaussian mixture model (GMM) cannot well describe the skewness distribution of the gray-level histogram of a liver CT slice, we propose a novel segmentation method for liver CT images by introducing the Johnson-SB mixture model (J(SB)MM). The Johnson-SB model not only has a flexible asymmetrical distribution but also covers a variety of other distributions as well. In this article, the parameter optimization formulas for J(SB)MM were derived by employing the expectation-maximization (EM) algorithm and maximum likelihood. The implementation process of the J(SB)MM-based segmentation algorithm is provided in detail. To make better use of the skewness of Johnson-SB and improve the segmentation accuracy, we devise an idea to divide the histogram into two parts and calculate the segmentation threshold for each part, respectively, which is called J(SB)MM-TDH. By analyzing and comparing the segmentation thresholds with different cluster numbers, it is illustrated that the segmentation threshold of J(SB)MM-TDH will tend to be stable with the increasing of cluster number, while that of GMM is sensitive to different cluster numbers. The proposed J(SB)MM-TDH is applied to segment four randomly obtained abdominal CT image sequences, and the segmentation results and robustness have been compared between J(SB)MM-TDH and GMM. It is verified that J(SB)MM-TDH has preferable segmentation results and better robustness than GMM for the segmentation of liver CT images.