A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images.
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 i...
| Published in: | BioMed Research International Vol. 2018; pp. 1 - 27 |
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| Main Authors: | , , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
8/9/2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131158297&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131158297 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/9/2018 vid: 2018 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 131158297 131158297 131158297 10.1155/2018/3815346 131158297 ppf: 1 ppct: 26 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images. aug: au: Zheng, Zhou Zhang, Xuechang Xu, Huafei Liang, Wang Zheng, Siming Shi, Yueding affil: Institute of Mechanical Engineering, Zhejiang University, Hangzhou 301127, China sug: subj: Liver Neoplasms Radiography Algorithms Evaluation Tomography, X-Ray Computed Methods Human Liver Neoplasms Diagnosis Radiography Methods ab: 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. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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