A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity.
Markov random field (MRF) model is an effective method for brain tissue classification, which has been applied in MR image segmentation for decades. However, it falls short of the expected classification in MR images with intensity inhomogeneity for the bias field is not considered in the formulatio...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 53; no. 1; pp. 23 - 36 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jan2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109773466&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109773466 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2015 vid: 53 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109773466 NLM25304717 2012869314 10.1007/s11517-014-1198-y NLM25304717 109773466 ppf: 23 ppct: 13 formats: fmt: @attributes: type: P tig: atl: A modified method for MRF segmentation and bias correction of MR image with intensity inhomogeneity. aug: au: Xie, Mei Gao, Jingjing Zhu, Chongjin Zhou, Yan sug: subj: Algorithms Image Interpretation, Computer Assisted Magnetic Resonance Imaging Analysis of Variance Gray Matter Pathology Human Software Statistics Brain Pathology ab: Markov random field (MRF) model is an effective method for brain tissue classification, which has been applied in MR image segmentation for decades. However, it falls short of the expected classification in MR images with intensity inhomogeneity for the bias field is not considered in the formulation. In this paper, we propose an interleaved method joining a modified MRF classification and bias field estimation in an energy minimization framework, whose initial estimation is based on k-means algorithm in view of prior information on MRI. The proposed method has a salient advantage of overcoming the misclassifications from the non-interleaved MRF classification for the MR image with intensity inhomogeneity. In contrast to other baseline methods, experimental results also have demonstrated the effectiveness and advantages of our algorithm via its applications in the real and the synthetic MR images. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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