التفاصيل البيبلوغرافية
العنوان: |
Gaussian Mixture Models and Model Selection for [18F] Fluorodeoxyglucose Positron Emission Tomography Classification in Alzheimer’s Disease |
المؤلفون: |
Li, Rui, Perneczky, Robert, Yakushev, Igor, Förster, Stefan, Kurz, Alexander, Drzezga, Alexander, Kramer, Stefan |
المساهمون: |
Aston, John AD |
المصدر: |
PLOS ONE, vol 10, iss 4 |
بيانات النشر: |
eScholarship, University of California |
سنة النشر: |
2015 |
المجموعة: |
University of California: eScholarship |
مصطلحات موضوعية: |
Information and Computing Sciences, Machine Learning, Bioengineering, Acquired Cognitive Impairment, Neurodegenerative, Alzheimer's Disease, Brain Disorders, Aging, Biomedical Imaging, Neurosciences, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Dementia, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Neurological, Aged, 80 and over, Alzheimer Disease, Brain, Female, Fluorodeoxyglucose F18, Humans, Male, Models, Theoretical, Normal Distribution, Positron-Emission Tomography, Radiopharmaceuticals, Sensitivity and Specificity |
الوصف: |
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. |
نوع الوثيقة: |
article in journal/newspaper |
وصف الملف: |
application/pdf |
اللغة: |
unknown |
العلاقة: |
qt998371dj; https://escholarship.org/uc/item/998371djTest |
الإتاحة: |
https://escholarship.org/uc/item/998371djTest |
حقوق: |
public |
رقم الانضمام: |
edsbas.6F84E0F8 |
قاعدة البيانات: |
BASE |