Registration of FA and T1-Weighted MRI Data of Healthy Human Brain Based on Template Matching and Normalized Cross-Correlation.
In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A te...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 4; pp. 774 - 786 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2013
|
| 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=104190859&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104190859 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2013 vid: 26 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104190859 88934654 10.1007/s10278-012-9561-8 NLM23288436 PMC3705011 104190859 ppf: 774 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Registration of FA and T1-Weighted MRI Data of Healthy Human Brain Based on Template Matching and Normalized Cross-Correlation. aug: au: Malinsky, Milos Peter, Roman Hodneland, Erlend Lundervold, Astri Lundervold, Arvid Jan, Jiri affil: Department of Biomedicine, Neuroinformatics and Image Analysis Laboratory, University of Bergen, Bergen Norway sug: subj: Magnetic Resonance Imaging Methods Brain Anatomy and Histology Radiographic Image Enhancement Methods Radiographic Image Interpretation, Computer-Assisted Methods Neurosciences Algorithms Evaluation Research Human Funding Source ab: In this work, we propose a new approach for three-dimensional registration of MR fractional anisotropy images with T1-weighted anatomy images of human brain. From the clinical point of view, this accurate coregistration allows precise detection of nerve fibers that is essential in neuroscience. A template matching algorithm combined with normalized cross-correlation was used for this registration task. To show the suitability of the proposed method, it was compared with the normalized mutual information-based B-spline registration provided by the Elastix software library, considered a reference method. We also propose a general framework for the evaluation of robustness and reliability of both registration methods. Both registration methods were tested by four evaluation criteria on a dataset consisting of 74 healthy subjects. The template matching algorithm has shown more reliable results than the reference method in registration of the MR fractional anisotropy and T1 anatomy image data. Significant differences were observed in the regions splenium of corpus callosum and genu of corpus callosum, considered very important areas of brain connectivity. We demonstrate that, in this registration task, the currently used mutual information-based parametric registration can be replaced by more accurate local template matching utilizing the normalized cross-correlation similarity measure. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|