Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease.
We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer's disease. In the work, the term "pattern" stands for a certain brain region that characteriz...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2014-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0122731 |
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| author | Rui Li Robert Perneczky Igor Yakushev Stefan Förster Alexander Kurz Alexander Drzezga Stefan Kramer Alzheimer’s Disease Neuroimaging Initiative |
| author_facet | Rui Li Robert Perneczky Igor Yakushev Stefan Förster Alexander Kurz Alexander Drzezga Stefan Kramer Alzheimer’s Disease Neuroimaging Initiative |
| author_sort | Rui Li |
| collection | DOAJ |
| description | We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer's disease. In the work, the term "pattern" stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called "region of interest (ROI)". In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is based on learning the characteristics of the given data, given some prior anatomical knowledge. A Gaussian Mixture Model (GMM) and model selection are combined to return a clustering of voxels that may serve for the definition of ROIs. Experiments on both an in-house dataset and data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) suggest that the proposed approach arrives at a better diagnosis than a merely anatomical approach or conventional statistical hypothesis testing. |
| format | Article |
| id | doaj-art-57fdbafe1d6c4b2fbe1b23067086565c |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-57fdbafe1d6c4b2fbe1b23067086565c2025-08-20T02:34:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032014-01-01104e012273110.1371/journal.pone.0122731Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease.Rui LiRobert PerneczkyIgor YakushevStefan FörsterAlexander KurzAlexander DrzezgaStefan KramerAlzheimer’s Disease Neuroimaging InitiativeWe present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer's disease. In the work, the term "pattern" stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called "region of interest (ROI)". In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is based on learning the characteristics of the given data, given some prior anatomical knowledge. A Gaussian Mixture Model (GMM) and model selection are combined to return a clustering of voxels that may serve for the definition of ROIs. Experiments on both an in-house dataset and data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) suggest that the proposed approach arrives at a better diagnosis than a merely anatomical approach or conventional statistical hypothesis testing.https://doi.org/10.1371/journal.pone.0122731 |
| spellingShingle | Rui Li Robert Perneczky Igor Yakushev Stefan Förster Alexander Kurz Alexander Drzezga Stefan Kramer Alzheimer’s Disease Neuroimaging Initiative Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease. PLoS ONE |
| title | Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease. |
| title_full | Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease. |
| title_fullStr | Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease. |
| title_full_unstemmed | Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease. |
| title_short | Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer's Disease. |
| title_sort | gaussian mixture models and model selection for 18f fluorodeoxyglucose positron emission tomography classification in alzheimer s disease |
| url | https://doi.org/10.1371/journal.pone.0122731 |
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