NDLI: A Likelihood and Local Constraint Level Set Model for Liver Tumor Segmentation from CT Volumes

Content Provider	IEEE Xplore Digital Library
Author	Changyang Li Xiuying Wang Eberl, S. Fulham, M. Yong Yin Jinhu Chen Feng, D.D.
Copyright Year	1964
Abstract	In computed tomography of liver tumors there is often heterogeneous density, weak boundaries, and the liver tumors are surrounded by other abdominal structures with similar densities. These pose limitations to accurate the hepatic tumor segmentation. We propose a level set model incorporating likelihood energy with the edge energy. The minimization of the likelihood energy approximates the density distribution of the target and the multimodal density distribution of the background that can have multiple regions. In the edge energy formulation, our edge detector preserves the ramp associated with the edges for weak boundaries. We compared our approach to the Chan-Vese and the geodesic level set models and the manual segmentation performed by clinical experts. The Chan-Vese model was not successful in segmenting hepatic tumors and our model outperformed the geodesic level set model. Our results on 18 clinical datasets showed that our algorithm had a Jaccard distance error of 14.4 ± 5.3%, relative volume difference of -8.1 ± 2.1%, average surface distance of 2.4 ± 0.8 mm, RMS surface distance of 2.9 ± 0.7 mm, and the maximum surface distance of 7.2 ± 3.1 mm.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	11
File Size	1048611
Starting Page	2967
Ending Page	2977
File Format	PDF
ISSN	00189294
Volume Number	60
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tumors Level set Image segmentation Image edge detection Liver Computed tomography Mathematical model likelihood Computed tomography (CT) edge detector liver/hepatic tumor segmentation level set function
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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