Abstract
The analysis of positron emission tomography (PET) scan image is challenging due to a high level of noise and a low resolution and also because differences between healthy and demented are very subtle. High dimensional classification methods based on PET have been proposed to automatically discriminate between normal control group (NC) patients and patients with Alzheimer’s disease (AD), with mild cognitive impairment (MCI), and mild cognitive impairment converting to Alzheimer’s disease (MCIAD ) (a group of patients that clearly degrades to AD). We developed a voxelbased method for volumetric image analysis. We performed 3 classification experiments AD vs CG, AD vs MCI, MCIAD vs MCI. We will also give a small demonstration of the presented method on a set of face images. This method is capable to extract information about the location of metabolic changes induced by Alzheimer’s disease that directly relies statistical features and brain regions of interest (ROIs). We produce “maps” to visualize the most informative regions of the brain and compare them with voxel-wise statistics. Using the mean intensity of about 2000 6 × 6 × 6mm patches, selected by the extracted map, as input for a classifier we obtain a classification rate of 95.5%.
Keywords: AD, Alzheimer’s disease, classification, Computer-aided diagnosis, machine learning, MCI, PET scan, random forest.
Current Alzheimer Research
Title:Alzheimer’s Disease Brain Areas: The Machine Learning Support for Blind Localization
Volume: 13 Issue: 5
Author(s): V. Vigneron, A. Kodewitz, A. M. Tome, S. Lelandais and E. Lang
Affiliation:
Keywords: AD, Alzheimer’s disease, classification, Computer-aided diagnosis, machine learning, MCI, PET scan, random forest.
Abstract: The analysis of positron emission tomography (PET) scan image is challenging due to a high level of noise and a low resolution and also because differences between healthy and demented are very subtle. High dimensional classification methods based on PET have been proposed to automatically discriminate between normal control group (NC) patients and patients with Alzheimer’s disease (AD), with mild cognitive impairment (MCI), and mild cognitive impairment converting to Alzheimer’s disease (MCIAD ) (a group of patients that clearly degrades to AD). We developed a voxelbased method for volumetric image analysis. We performed 3 classification experiments AD vs CG, AD vs MCI, MCIAD vs MCI. We will also give a small demonstration of the presented method on a set of face images. This method is capable to extract information about the location of metabolic changes induced by Alzheimer’s disease that directly relies statistical features and brain regions of interest (ROIs). We produce “maps” to visualize the most informative regions of the brain and compare them with voxel-wise statistics. Using the mean intensity of about 2000 6 × 6 × 6mm patches, selected by the extracted map, as input for a classifier we obtain a classification rate of 95.5%.
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Cite this article as:
Vigneron V., Kodewitz A., Tome M. A., Lelandais S. and Lang E., Alzheimer’s Disease Brain Areas: The Machine Learning Support for Blind Localization, Current Alzheimer Research 2016; 13 (5) . https://dx.doi.org/10.2174/1567205013666160314144822
DOI https://dx.doi.org/10.2174/1567205013666160314144822 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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Alzheimer's disease (AD) poses a significant global health challenge, with an increasing number of individuals affected yearly. Deep learning, a subfield of artificial intelligence, has shown immense potential in various domains, including healthcare. This thematic issue of Current Alzheimer Research explores the application of deep learning techniques in advancing our ...read more
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