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Alzheimer’s disease early detection from sparse data using brain importance maps
Kodewitz, Andreas
Lelandais, Sylvie
Montagne, Christophe
Vigneron, Vincent
University of Evry. IBISC Laboratory

Data: 2013
Resum: Statistical methods are increasingly used in the analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. We will present a method to extract information about the location of metabolic changes induced by Alzheimer’s disease based on a machine learning approach that directly links features and brain areas to search for regions of interest (ROIs). This approach has the advantage over voxel-wise statistics to also consider the interactions between the features/voxels. We produce “maps” to visualize the most informative regions of the brain and compare the maps created by our approach with voxel-wise statistics. In classification experiments, using the extracted map, we achieved classification rates of up to 95. 5%.
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès
Document: article ; recerca ; publishedVersion
Matèria: Nuclear Imaging ; Brain ; Computer-aided diagnosis ; Machine learning ; Alzheimer’s disease
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 12, Núm. 1 (2013) , p. 42-56, ISSN 1577-5097

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