NDLI: Automatic Liver Segmentation from CT Images Using Adaptive Fast Marching Method

Content Provider	IEEE Xplore Digital Library
Author	Xiao Song Ming Cheng Boliang Wang Shaohui Huang Xiaoyang Huang
Copyright Year	2013
Description	Author affiliation: Coll. of Comput. & Inf. Technol., Nanyang Normal Univ., Nanyang, China (Xiao Song) \|\| Coll. of Inf. Sci. & Technol., Xiamen Univ., Xiamen, China (Ming Cheng; Boliang Wang; Shaohui Huang; Xiaoyang Huang)
Abstract	Liver segmentation is the fundamental step in computer-aided liver disease diagnosis and surgery planning. In this study, we developed a fully automatic liver extraction scheme based on an adaptive fast marching method (FMM). Firstly, a thresholding operation was applied to remove the ribs, spines and kidneys. Followed by a smooth filter for noise reduction. Secondly, a nonlinear gray scale converter was used to enhance the contrast of the liver parenchyma. The enhanced image is then eroded with 3-voxel radius so that small regions are deleted. The seed points located in the liver were selected automatically. Finally, using the processed image as a speed function, FMM was employed to generate the liver contour. Clinical validation has performed on 30 abdominal computed tomography (CT) datasets. The proposed algorithm achieved an overall true positive rate (TPR) of 0.98. It takes about 0.30 s for a 512×512-pixel slice. The method has been applied successfully for fast and accurate liver segmentation.
Starting Page	897
Ending Page	900
File Size	765423
Page Count	4
File Format	PDF
ISBN	9780769550503
DOI	10.1109/ICIG.2013.181
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	fast marching method Image segmentation computer-aided Accuracy Computed tomography Level set Liver Surgery liver segmentation Educational institutions
Content Type	Text
Resource Type	Article

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