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

• Published in: Journal of healthcare engineering 2022, Vol.2022, p.5654424
• Main Authors: Dun, Yueqin, Kong, Yu
• Format: Article
• Language: English
• Subjects: Algorithms; Humans; Image Processing, Computer-Assisted - methods; Liver - diagnostic imaging; Normal Distribution; Tomography, X-Ray Computed - methods
• ISSN: 2040-2309

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 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 MM were derived by employing the expectation-maximization (EM) algorithm and maximum likelihood. The implementation process of the J 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 MM-TDH. By analyzing and comparing the segmentation thresholds with different cluster numbers, it is illustrated that the segmentation threshold of J 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 MM-TDH is applied to segment four randomly obtained abdominal CT image sequences, and the segmentation results and robustness have been compared between J MM-TDH and GMM. It is verified that J MM-TDH has preferable segmentation results and better robustness than GMM for the segmentation of liver CT images.