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- Publisher Website: 10.1016/j.patcog.2017.11.019
- Scopus: eid_2-s2.0-85040374830
- WOS: WOS:000424853800027
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Article: Weighted variational model for selective image segmentation with application to medical images
Title | Weighted variational model for selective image segmentation with application to medical images |
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Authors | |
Keywords | Selective segmentation Mumford-Shah model Iterative algorithm Medical images Thresholding |
Issue Date | 2018 |
Citation | Pattern Recognition, 2018, v. 76, p. 367-379 How to Cite? |
Abstract | © 2017 Elsevier Ltd Selective image segmentation is an important topic in medical imaging and real applications. In this paper, we propose a weighted variational selective image segmentation model which contains two steps. The first stage is to obtain a smooth approximation related to Mumford-Shah model to the target region in the input image. Using weighted function, the approximation provides a larger value for the target region and smaller values for other regions. In the second stage, we make use of this approximation and perform a thresholding procedure to obtain the object of interest. The approximation can be obtained by the alternating direction method of multipliers and the convergence analysis of the method can be established. Experimental results for medical image selective segmentation are given to demonstrate the usefulness of the proposed method. We also do some comparisons and show that the performance of the proposed method is more competitive than other testing methods. |
Persistent Identifier | http://hdl.handle.net/10722/276571 |
ISSN | 2023 Impact Factor: 7.5 2023 SCImago Journal Rankings: 2.732 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liu, Chunxiao | - |
dc.contributor.author | Ng, Michael Kwok Po | - |
dc.contributor.author | Zeng, Tieyong | - |
dc.date.accessioned | 2019-09-18T08:34:00Z | - |
dc.date.available | 2019-09-18T08:34:00Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Pattern Recognition, 2018, v. 76, p. 367-379 | - |
dc.identifier.issn | 0031-3203 | - |
dc.identifier.uri | http://hdl.handle.net/10722/276571 | - |
dc.description.abstract | © 2017 Elsevier Ltd Selective image segmentation is an important topic in medical imaging and real applications. In this paper, we propose a weighted variational selective image segmentation model which contains two steps. The first stage is to obtain a smooth approximation related to Mumford-Shah model to the target region in the input image. Using weighted function, the approximation provides a larger value for the target region and smaller values for other regions. In the second stage, we make use of this approximation and perform a thresholding procedure to obtain the object of interest. The approximation can be obtained by the alternating direction method of multipliers and the convergence analysis of the method can be established. Experimental results for medical image selective segmentation are given to demonstrate the usefulness of the proposed method. We also do some comparisons and show that the performance of the proposed method is more competitive than other testing methods. | - |
dc.language | eng | - |
dc.relation.ispartof | Pattern Recognition | - |
dc.subject | Selective segmentation | - |
dc.subject | Mumford-Shah model | - |
dc.subject | Iterative algorithm | - |
dc.subject | Medical images | - |
dc.subject | Thresholding | - |
dc.title | Weighted variational model for selective image segmentation with application to medical images | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.patcog.2017.11.019 | - |
dc.identifier.scopus | eid_2-s2.0-85040374830 | - |
dc.identifier.volume | 76 | - |
dc.identifier.spage | 367 | - |
dc.identifier.epage | 379 | - |
dc.identifier.isi | WOS:000424853800027 | - |
dc.identifier.issnl | 0031-3203 | - |