Automatic Liver Vessel Segmentation Using 3D Region Growing and Hybrid Active Contour Model_.pdf (5.03 MB)
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Automatic liver vessel segmentation using 3D region growing and hybrid active contour model

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journal contribution
posted on 21.05.2018, 12:24 by Ye-zhan Zeng, Sheng-hui Liao, Ping Tang, Yu-qian Zhao, Miao Liao, Yan Chen, Yi-xiong Liang
This paper proposes a new automatic method for liver vessel segmentation by exploiting intensity and shape constraints of 3D vessels. The core of the proposed method is to apply two different strategies: 3D region growing facilitated by bi-Gaussian filter for thin vessel segmentation, and hybrid active contour model combined with K-means clustering for thick vessel segmentation. They are then integrated to generate final segmentation results. The proposed method is validated on abdominal computed tomography angiography (CTA) images, and obtains an average accuracy, sensitivity, specificity, Dice, Jaccard, and RMSD of 98.2%, 68.3%, 99.2%, 73.0%, 66.1%, and 2.56 mm, respectively. Experimental results show that our method is capable of segmenting complex liver vessels with more continuous and complete thin vessel details, and outperforms several existing 3D vessel segmentation algorithms.

Funding

This work is supported by the National Natural Science Foundation of China (Grant nos. 61772555, 61379107, 61772556, 61172184, and 61702179), China Postdoctoral Science Foundation (Grant no. 2012M521554), Program for Hunan Province Science and Technology Basic Construction (Grant no. 20131199), Hunan Provincial Natural Science Foundation of China (Grant no. 2017JJ3091), and Scientific Research Fund of Hunan Provincial Education Department (Grant no.17C0645).

History

School

  • Science

Department

  • Computer Science

Published in

Computers in Biology and Medicine

Volume

97

Pages

63 - 73

Citation

ZENG, Y-Z. ... et al, 2018. Automatic liver vessel segmentation using 3D region growing and hybrid active contour model. Computers in Biology and Medicine, 97, pp.63-73.

Publisher

© Elsevier

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2018-04-24

Notes

This paper was accepted for publication in the journal Computers in Biology and Medicine, and the definitive published version is available at https://doi.org/10.1016/j.compbiomed.2018.04.014

ISSN

0010-4825

Language

en