We present a multi-region analysis of FDG-PET data for classification of subjects from the Alzheimer's Disease Neuroimaging Initiative. Image data were obtained from 69 healthy controls, 71 AD patients, and 147 patients with a baseline diagnosis of MCI. Anatomical segmentations were automatically generated in the native MRI-space of each subject, and the mean signal intensity per cubic millimetre in each region was extracted from the FDG-PET images. Using a support vector machine classifier, we achieve excellent discrimination between AD patients and HC (area under ROC curve 90%), as well as between MCI patients and HC (area under ROC curve 75%). Using FDG-PET, a technique which is often used clinically in the workup of dementia patients, we achieve results which are comparable with those obtained using data from research-quality MRI, or biomarkers obtained invasively from the cerebrospinal fluid.
Available in proceedings at http://www.biomedical-image-analysis.co.uk/images/stories/gray-posters2-77.pdf
pubs.doc.ic.ac.uk: built & maintained by Ashok Argent-Katwala.