Hepatic vessel segmentation based on animproved 3D region growing algorithm. Issue 3 (April 2020)

• Record Type: Journal Article
• Authors: Zhang, Huahai; Bai, Peirui; Min, Xiaolin; Liu, Qingyi; Ren, Yande; Li, Hui; Li, Yixuan
• Is Part Of: Journal of physics. Volume 1486:Issue 3(2020)
• Publication Date: 2020-04
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1486/3/032038
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Hepatic vessel segmentation of CT image is of great importance in the computer aided diagnosis. This paper proposes an automatic segmentation method of 3D vessel CT images to obtain better segmentation results. First, the single Gaussian kernel of Hessian matrix in the Jerman's algorithm is replaced by bi-Gaussian kernel. Then, a histogram-based method is adopted to adaptively estimate the threshold value of the region growing. Finally, a new scheme is proposed forautomatically searching seed points of the region growing. The experimental results show that the proposed method achieves a significant enhancement of hepatic vessels segmentation with an average accuracy 98.1%.