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.
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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.
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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
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DOI: https://doi.org/10.1007/978-981-10-7521-6_23
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