A Novel Statistical Approach for Brain MR Images Segmentation Based on Relaxation Times.
Brain tissue segmentation in Magnetic Resonance Imaging is useful for a wide range of applications. Classical approaches exploit the gray levels image and implement criteria for differentiating regions. Within this paper a novel approach for brain tissue joint segmentation and classification is pres...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 14 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/21/2015
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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=113630223&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113630223 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/21/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 113630223 113630223 113630223 10.1155/2015/154614 113630223 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: A Novel Statistical Approach for Brain MR Images Segmentation Based on Relaxation Times. aug: au: Baselice, Fabio Ferraioli, Giampaolo Pascazio, Vito affil: Dipartimento di Ingegneria, Università di Napoli Parthenope, Centro Direzionale di Napoli, Isola C4, 80143 Napoli, Italy sug: subj: Magnetic Resonance Imaging Methods Brain Models, Statistical Utilization Human ab: Brain tissue segmentation in Magnetic Resonance Imaging is useful for a wide range of applications. Classical approaches exploit the gray levels image and implement criteria for differentiating regions. Within this paper a novel approach for brain tissue joint segmentation and classification is presented. Starting from the estimation of proton density and relaxation times, we propose a novel method for identifying the optimal decision regions. The approach exploits the statistical distribution of the involved signals in the complex domain. The technique, compared to classical threshold based ones, is able to globally improve the classification rate. The effectiveness of the approach is evaluated on both simulated and real datasets. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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