Abstract
In this paper, a new coarse-to-fine framework is proposed for automatic liver segmentation on abdominal computed tomography (CT) images. The framework consists of two steps including rough segmentation and refined segmentation. The rough segmentation is implemented based on histogram thresholding and the largest connected component algorithm. Firstly, gray value range of the liver is obtained from image histogram, then the liver area is extracted from the rest of an image according to the largest connected component algorithm. The refined segmentation is performed based on the improved GrowCut (IGC) algorithm, which generates the label seeds automatically. The experimental results show that the proposed framework can efficiently segment the liver on CT images.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yang, X., Yu, H., et al.: A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points. Comput. Methods Programs Biomed. 113(1), 69–79 (2014)
Foruzan, A., Chen, Y., et al.: Segmentation of liver in low-contrast images using K-Means clustering and geodesic active contour algorithms. IEICE Trans. Inf. Syst. 96, 798–807 (2013)
Zhang, X., Tian, J., Deng, K., et al.: Automaticliver segmentation using a statistical shape model with optimal surface detection. IEEE Trans. Biomed. Eng. 57(10), 2622–2626 (2010)
Li, X., Luo, S., Li, J.: Liver segmentation from CT image using fuzzy clustering and level set. J. Sig. Inf. Process. 4(3), 36–42 (2013)
Li, B.N., Chui, C., Chang, S., et al.: Integrating spatial fuzzy Clustering with level set methods for automated medical image segmentation. Comput. Biol. Med. 41(1), 1–10 (2011)
Chen, S., Zhang, D.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(4), 1907–1916 (2004)
Kannan, S.R., Ramathilagam, S., Sathya, A.: Effective fuzzy C-means based kernel function in segmenting medical images. Comput. Biol. Med. 40(6), 572–579 (2010)
Zayane, O., Jouini, B., Mahjoub, M.A.: Automatic liver segmentation method in CT images. Can. J. Image Process. Comput. Vis. 5(1), 85–92 (2011)
Vezhnevets, V., Konouchine, V.: GrowCut: interactive multi-label ND image segmentation by cellular automata. In: Graphicon Novosibirsk Akademgorodok, pp. 150–156 (2005)
Hamamci, A., Unal, G., Kucuk, N., et al.: Cellular automata segmentation of brain tumors on post contrast MR Images. Med. Image Comput. Comput. Assist. Interv. MICCAI 13(3), 137–146 (2010)
Yamasaki, T., Chen, T., Yagi, M., et al.: GrowCut-based Fast Tumor Segmentation for 3D Magnetic Resonance Images. SPIE Medical Imaging, International Society for Optics and Photonics 8314, 208–220 (2012)
Song, H., Zhang, Q., Wang, S.: Liver segmentation based on SKFCM and Improved GrowCut for CT images. In: IEEE International Conference of Bioinformatics and Biomedicine (BIBM), pp. 331–334 (2014)
Acknowledgement
This work was supported by the National Hi-Tech Research and Development Program (2015AA043203), and the National Science Foundation Program of China (81430039, 61672099, 81430039, 61501030). This study received financial support from Frontier and interdisciplinary innovation program of Beijing Institute of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Celik, T., Song, H., Chen, L., Yang, J. (2018). Automatic Liver Segmentation on CT Images. In: Sun, S., Chen, N., Tian, T. (eds) Signal and Information Processing, Networking and Computers. ICSINC 2017. Lecture Notes in Electrical Engineering, vol 473. Springer, Singapore. https://doi.org/10.1007/978-981-10-7521-6_23
Download citation
DOI: https://doi.org/10.1007/978-981-10-7521-6_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-7520-9
Online ISBN: 978-981-10-7521-6
eBook Packages: EngineeringEngineering (R0)