Feature Extraction in PET Images for the Diagnosis of Alzheimer's Disease
Fred, A. L. N.
Feature Extraction in PET Images for the Diagnosis of Alzheimer's Disease, Proc INSTICC International Conf. on Pattern Recognition Applications and Methods - ICPRAM, Angers, Loire Valley, France, Vol. 0, pp. 561 - 568, March, 2014.
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Alzheimer's disease accounts for an estimated 60% to 80% of cases of dementia and its victims are mainly elderly people. Recently, several computer-aided diagnosis systems have been developed, based on extracting information from FDG-PET scans. 3-dimensional FDG-PET images, under a voxel-as-feature approach, lead to high-dimensional feature spaces, which results in system performance problems. In order to reduce the dimensionality of these images, multi-scale methods may be used as feature extraction. We propose a multiscale approach for feature extraction of 3-dimensional images to improve the performance of a diagnosis system using clustering techniques. To evaluate the performance of our approach we applied it to a database obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) and compare it with Gaussian pyramid technique. Experimental results have shown that the proposed approach is a good option for image feature reduction, outperforming the Gaussian pyramid technique.