학술논문
Automatic classification of Alzheimer's Disease vs. Frontotemporal dementia: A spatial decision tree approach with FDG-PET
Document Type
Conference
Source
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on. :408-411 May, 2008
Subject
Language
ISSN
1945-7928
1945-8452
1945-8452
Abstract
We introduce a novel approach for the automatic classification of FDG-PET scans of subjects with Alzheimers Disease (AD) and Frontotemporal dementia (FTD). Unlike previous work in the literature which focuses on principal component analysis and predefined regions of interest, we propose the combined use of information gain and spatial proximity to group cortical pixels into empirically determined regions that can best separate the two diseases. These regions are then used as attributes in a decision tree learning framework. We demonstrate that the proposed method provides better classification accuracy compared to other methods on a group of 48 autopsy confirmed AD and FTD patients.