Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.eswa.2012.02.095
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dc.titleA new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images
dc.contributor.authorLi, B.N.
dc.contributor.authorChui, C.K.
dc.contributor.authorChang, S.
dc.contributor.authorOng, S.H.
dc.date.accessioned2014-04-24T07:19:08Z
dc.date.available2014-04-24T07:19:08Z
dc.date.issued2012-08
dc.identifier.citationLi, B.N., Chui, C.K., Chang, S., Ong, S.H. (2012-08). A new unified level set method for semi-automatic liver tumor segmentation on contrast-enhanced CT images. Expert Systems with Applications 39 (10) : 9661-9668. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2012.02.095
dc.identifier.issn09574174
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/50846
dc.description.abstractObjective: 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 ± 1.09 mm, and the maximum contour distance (MCD) is 4.29 ± 2.75 mm. 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 ± 0.38 mm, and the maximum surface distance (MSD) is 8.66 ± 3.17 mm. 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. © 2012 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.eswa.2012.02.095
dc.sourceScopus
dc.subjectContrast-enhanced computed tomography
dc.subjectLevel set methods
dc.subjectLiver tumor segmentation
dc.subjectMedical image computing
dc.typeArticle
dc.contributor.departmentMECHANICAL ENGINEERING
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1016/j.eswa.2012.02.095
dc.description.sourcetitleExpert Systems with Applications
dc.description.volume39
dc.description.issue10
dc.description.page9661-9668
dc.description.codenESAPE
dc.identifier.isiut000303281800118
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