Alzheimer's disease early detection from sparse data using brain importance maps

ELCVIA Electronic Letters on Computer Vision and Image Analysis

Sylvie Lelandais¹  Vincent Vigneron¹  Christophe Montagne¹  Andreas Kodewitz¹ 

[1] University of Evry

关键词: Statistical Pattern Recognition;    Machine Learning and Data Mining;    Medical Diagnosis;    Medical Image Analysis;   

DOI  :  

学科分类：计算机科学（综合）

来源: ELCVIA

【 摘 要 】

Statistical methods are increasingly used in the analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. We will demonstrate a method to extract information about the location of metabolic changes induced by Alzheimer’s disease based on a machine learning approach that directly relies features and brain areas to search for regions of interest (ROIs). This approach has the advantage over voxel-wise statistics to consider also 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 maps, we achieved classification rates of up to 95.5%.

【 授权许可】

Unknown