@inproceedings{a34eeb18367c4fc283f2d4177623e1fe,
title = "Automatic Liver Segmentation on CT Images",
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.",
keywords = "CT images, Histogram thresholding, Improved Grow-Cut, Liver segmentation",
author = "Torecan Celik and Hong Song and Lei Chen and Jian Yang",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Singapore Pte Ltd.; 3rd International Conference on Signal and Information Processing, Networking and Computers, ICSINC 2017 ; Conference date: 13-09-2017 Through 15-09-2017",
year = "2018",
doi = "10.1007/978-981-10-7521-6_23",
language = "English",
isbn = "9789811075209",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Verlag",
pages = "189--196",
editor = "Songlin Sun and Na Chen and Tao Tian",
booktitle = "Signal and Information Processing, Networking and Computers - Proceedings of the 3rd International Conference on Signal and Information Processing, Networking and Computers, ICSINC",
address = "Germany",
}