Paper Info
Title
A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images
Abstract
Objective: Computerized liver tumor segmentation on computed tomography (CT) images is a challenging problem. Level set methods have been proposed for CT liver and tumor segmentation. However, the common models using image gradient or region competition have inherent drawbacks, and are not very robust for liver tumor segmentation. Methods: We propose a new unified level set model to integrate image gradient, region competition and prior information for CT liver tumor segmentation. The probabilistic distribution of liver tumors is estimated by unsupervised fuzzy clustering, and is utilized to enhance the object indication function, define the directional balloon force and regulate region competition. This unified model has been evaluated on 25 two-dimensional (2D) CT scans and 4 three-dimensional (3D) CT scans with 10 tumors. Results: For the 2D dataset, the area overlapping error (AOE) is 12.75+/-5.76%, the relative area difference (RAD) is -4.28+/-9.58%, the average contour distance (ACD) is 1.66+/-1.09mm, and the maximum contour distance (MCD) is 4.29+/-2.75mm. For the 3D dataset, the volume overlapping error (VOE) is 26.31+/-5.79%, the relative volume difference (RVD) is -10.64+/-7.55%, the average surface distance (ASD) is 1.06+/-0.38mm, and the maximum surface distance (MSD) is 8.66+/-3.17mm. All results are competitive with that of the state-of-the-art methods. Conclusion: The new unified level set model is an effective solution for liver tumor segmentation on contrast-enhanced CT images.
Year
DOI
Venue
2012
10.1016/j.eswa.2012.02.095
Expert Syst. Appl.
Keywords
Field
DocType
ct scan,contrast-enhanced ct image,liver tumor segmentation,semi-automatic liver tumor segmentation,liver tumor,new unified level set,ct liver,image gradient,computerized liver tumor segmentation,ct liver tumor segmentation,region competition
Fuzzy clustering,Computer vision,Image gradient,Scale-space segmentation,Computer science,Level set method,Segmentation,Level set,Image segmentation,Medical image computing,Artificial intelligence
Journal
Volume
Issue
ISSN
39
10
0957-4174
Citations 
PageRank 
References 
25
1.20
13
Authors
4
Name
Order
Citations
PageRank
Bing Nan Li124018.77
Chee Kong Chui21439.66
Stephen Chang31348.90
Sim Heng Ong442644.63