Preprocessing of 18F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution.
18F-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson'disease s (PD) that allows us to examine postsynaptic dopamine D2/3 receptors. Like other neuroimaging modalities used for PD diagnosis, most of the total intensity of 18F-DMFP-PET images is concentrated in the striatum. Ho...
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 10 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Frontiers Media S.A.
10/9/2017
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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=125575844&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 125575844 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 10/9/2017 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 125575844 10.3389/fnagi.2017.00326 125575844 ppf: 1 ppct: 9 formats: tig: atl: Preprocessing of 18F-DMFP-PET Data Based on Hidden Markov Random Fields and the Gaussian Distribution. aug: au: Segovia, Fermín Górriz, Juan M. Ramírez, Javier Martínez-Murcia, Francisco J. Salas-Gonzalez, Diego affil: Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain sug: ab: 18F-DMFP-PET is an emerging neuroimaging modality used to diagnose Parkinson'disease s (PD) that allows us to examine postsynaptic dopamine D2/3 receptors. Like other neuroimaging modalities used for PD diagnosis, most of the total intensity of 18F-DMFP-PET images is concentrated in the striatum. However, other regions can also be useful for diagnostic purposes. An appropriate delimitation of the regions of interest contained in 18F-DMFP-PET data is crucial to improve the automatic diagnosis of PD. In this manuscript we propose a novel methodology to preprocess 18F-DMFP-PET data that improves the accuracy of computer aided diagnosis systems for PD. First, the data were segmented using an algorithm based on Hidden Markov Random Field. As a result, each neuroimage was divided into 4 maps according to the intensity and the neighborhood of the voxels. The maps were then individually normalized so that the shape of their histograms could be modeled by a Gaussian distribution with equal parameters for all the neuroimages. This approach was evaluated using a dataset with neuroimaging data from 87 parkinsonian patients. After these preprocessing steps, a Support Vector Machine classifier was used to separate idiopathic and non-idiopathic PD. Data preprocessed by the proposed method provided higher accuracy results than the ones preprocessed with previous approaches. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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