Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIP.2011.2162738
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dc.titleIntegrating segmentation information for improved mrf-based elastic image registration
dc.contributor.authorMahapatra, D.
dc.contributor.authorSun, Y.
dc.date.accessioned2014-10-07T04:30:39Z
dc.date.available2014-10-07T04:30:39Z
dc.date.issued2012-01
dc.identifier.citationMahapatra, D., Sun, Y. (2012-01). Integrating segmentation information for improved mrf-based elastic image registration. IEEE Transactions on Image Processing 21 (1) : 170-183. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2011.2162738
dc.identifier.issn10577149
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/82547
dc.description.abstractIn this paper, we propose a method to exploit segmentation information for elastic image registration using a Markov-random-field (MRF)-based objective function. MRFs are suitable for discrete labeling problems, and the labels are defined as the joint occurrence of displacement fields (for registration) and segmentation class probability. The data penalty is a combination of the image intensity (or gradient information) and the mutual dependence of registration and segmentation information. The smoothness is a function of the interaction between the defined labels. Since both terms are a function of registration and segmentation labels, the overall objective function captures their mutual dependence. A multiscale graph-cut approach is used to achieve subpixel registration and reduce the computation time. The user defines the object to be registered in the floating image, which is rigidly registered before applying our method. We test our method on synthetic image data sets with known levels of added noise and simulated deformations, and also on natural and medical images. Compared with other registration methods not using segmentation information, our proposed method exhibits greater robustness to noise and improved registration accuracy. © 2011 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TIP.2011.2162738
dc.sourceScopus
dc.subjectCombined registration and segmentation (CRS)
dc.subjectlabels
dc.subjectMarkov random fields (MRFs)
dc.subjectnatural and medical images
dc.subjectobject of interest (OOI)
dc.subjectsimulated deformations
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/TIP.2011.2162738
dc.description.sourcetitleIEEE Transactions on Image Processing
dc.description.volume21
dc.description.issue1
dc.description.page170-183
dc.description.codenIIPRE
dc.identifier.isiut000298325500014
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