Open AccessHepatic vessel segmentation based on animproved 3D region growing algorithm
Author(s) -
Huahai Zhang,
Peirui Bai,
Xiaolin Min,
Qingyi Liu,
Yande Ren,
Hui Li,
Yixuan Li
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1486/3/032038
Subject(s) - hessian matrix , segmentation , region growing , kernel (algebra) , histogram , image segmentation , artificial intelligence , scale space segmentation , computer science , pattern recognition (psychology) , gaussian , algorithm , computer vision , image (mathematics) , mathematics , physics , combinatorics , quantum mechanics
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%.