Features-based approach for Alzheimer's disease diagnosis using visual pattern of water diffusion in Tensor Diffusion Imaging
Autor: | Michele Aliara, Gwénaëlle Catheline, Chokri Ben Amar, Jenny Benois-Pineau, Olfa Ben Ahmed |
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Přispěvatelé: | Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Département de Génie Électrique de Sfax [ENIS] (CEM Lab - ENIS), École Nationale d'Ingénieurs de Sfax | National School of Engineers of Sfax (ENIS), Service de médecine nucléaire, CHU Bordeaux [Bordeaux], Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), Ben Ahmed, Olfa |
Jazyk: | angličtina |
Rok vydání: | 2015 |
Předmět: |
Computer science
hippocampus Speech recognition MD maps [INFO.INFO-IM] Computer Science [cs]/Medical Imaging [INFO] Computer Science [cs] SVMs Atrophy Neuroimaging Tensor (intrinsic definition) medicine [INFO.INFO-IM]Computer Science [cs]/Medical Imaging [INFO]Computer Science [cs] Water diffusion Alzheimer’s dis- ease Features business.industry Pattern recognition CHFs medicine.disease Support vector machine Diffusion imaging [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] Feature (computer vision) DTI [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Visual patterns Artificial intelligence business AD-related signature |
Zdroj: | ICIP ICIP, Sep 2015, Quebec, Canada. pp.5, 2015 |
Popis: | International audience; In this paper, we propose a feature-based classification framework for Alzheimer’s disease (AD) recognition usingTensor Diffusion Imaging (DTI). The main contribution consists in considering the visual pattern of water molecules diffusion in the most involved region in AD (hippocampal area). We use the Circular Harmonic Functions (CHFs) and the Bag-of-Visual-Words approach to build an AD related-signature. The experiments were accomplished first with a subset of participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset and then with the DTI scans of a French epidemiological study: ”Bordeaux-3City”. Experimental results demonstrate that our features-based method applied onthe MD maps is able to capture the AD-related atrophy and then classify between AD subjects. |
Databáze: | OpenAIRE |
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