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A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images
Joint Authors
Zheng, Zhou
Zhang, Xuechang
Xu, Huafei
Liang, Wang
Zheng, Siming
Shi, Yueding
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-26, 26 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-09
Country of Publication
Egypt
No. of Pages
26
Main Subjects
Abstract EN
Accurate and reliable segmentation of liver tissue and liver tumor is essential for the follow-up of hepatic diagnosis.
In this paper, we present a method for liver segmentation and a method for liver tumor segmentation.
The two methods are grounded on a novel unified level set method (LSM), which incorporates both region information and edge information to evolve the contour.
This level set framework is more resistant to edge leakage than the single-information driven LSMs for liver segmentation and surpasses many other models for liver tumor segmentation.
Specifically, for liver segmentation, a hybrid image preprocessing scheme is used first to convert an input CT image into a binary image.
Then with manual setting of a few seed points on the obtained binary image, the following region-growing is performed to extract a rough liver region with no leakage.
The unified LSM is proposed at last to refine the segmentation result.
For liver tumor segmentation, a local intensity clustering based LSM coupled with hidden Markov random field and expectation-maximization (HMRF-EM) algorithm is applied to construct an enhanced edge indicator for the unified LSM.
With this development, expected segmentation results can be obtained via the unified LSM, even for complex tumors.
The two methods were evaluated with various datasets containing a local hospital dataset, the public datasets SLIVER07, 3Dircadb, and MIDAS via five measures.
The proposed liver segmentation method outperformed other previous semiautomatic methods on the SLIVER07 dataset and required less interaction.
The proposed liver tumor segmentation method was also competitive with other state-of-the-art methods in both accuracy and efficiency on the 3Dircadb database.
Our methods are evaluated to be accurate and efficient, which allows their adoptions in clinical practice.
American Psychological Association (APA)
Zheng, Zhou& Zhang, Xuechang& Xu, Huafei& Liang, Wang& Zheng, Siming& Shi, Yueding. 2018. A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images. BioMed Research International،Vol. 2018, no. 2018, pp.1-26.
https://search.emarefa.net/detail/BIM-1126116
Modern Language Association (MLA)
Zheng, Zhou…[et al.]. A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images. BioMed Research International No. 2018 (2018), pp.1-26.
https://search.emarefa.net/detail/BIM-1126116
American Medical Association (AMA)
Zheng, Zhou& Zhang, Xuechang& Xu, Huafei& Liang, Wang& Zheng, Siming& Shi, Yueding. A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images. BioMed Research International. 2018. Vol. 2018, no. 2018, pp.1-26.
https://search.emarefa.net/detail/BIM-1126116
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126116