Robust Intensity Standardization in Brain Magnetic Resonance Images.
The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient o...
| Publicado en: | Journal of Digital Imaging Vol. 28; no. 6; pp. 727 - 738 |
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| Autores principales: | , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2015
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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=110813325&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110813325 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2015 vid: 28 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 110813325 110813325 110813325 10.1007/s10278-015-9782-8 NLM25708893 PMC4636718 110813325 ppf: 727 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Robust Intensity Standardization in Brain Magnetic Resonance Images. aug: au: De Nunzio, Giorgio Cataldo, Rosella Carlà, Alessandra sug: subj: Magnetic Resonance Imaging Methods Brain Magnetic Resonance Imaging Standards Radiographic Magnification Standards Alzheimer's Disease Diagnosis Cognition Disorders Diagnosis Brain Pathology Software Comparative Studies Confidence Intervals Descriptive Statistics Middle Age Aged Aged, 80 and Over Female Male Human Funding Source Clinical Assessment Tools Questionnaires Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: The paper is focused on a tiSsue-Based Standardization Technique (SBST) of magnetic resonance (MR) brain images. Magnetic Resonance Imaging intensities have no fixed tissue-specific numeric meaning, even within the same MRI protocol, for the same body region, or even for images of the same patient obtained on the same scanner in different moments. This affects postprocessing tasks such as automatic segmentation or unsupervised/supervised classification methods, which strictly depend on the observed image intensities, compromising the accuracy and efficiency of many image analyses algorithms. A large number of MR images from public databases, belonging to healthy people and to patients with different degrees of neurodegenerative pathology, were employed together with synthetic MRIs. Combining both histogram and tissue-specific intensity information, a correspondence is obtained for each tissue across images. The novelty consists of computing three standardizing transformations for the three main brain tissues, for each tissue class separately. In order to create a continuous intensity mapping, spline smoothing of the overall slightly discontinuous piecewise-linear intensity transformation is performed. The robustness of the technique is assessed in a post hoc manner, by verifying that automatic segmentation of images before and after standardization gives a high overlapping (Dice index >0.9) for each tissue class, even across images coming from different sources. Furthermore, SBST efficacy is tested by evaluating if and how much it increases intertissue discrimination and by assessing gaussianity of tissue gray-level distributions before and after standardization. Some quantitative comparisons to already existing different approaches available in the literature are performed. 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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