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Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization

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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Submitted on : Monday, January 29, 2018 - 10:25:57 AM
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Vincent Vigneron, Andreas Kodewitz, Ana Maria Tome, Sylvie Lelandais, Elmar Lang. Alzheimer's Disease Brain Areas: The Machine Learning Support for Blind Localization. Current Alzheimer Research, Bentham Science Publishers, 2016, 13 (5), pp.498--508. ⟨10.2174/1567205013666160314144822⟩. ⟨hal-01695194⟩



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