Creating and sharing knowledge for telecommunications

Feature Extraction in PET Images for the Diagnosis of Alzheimer's Disease

Duarte, J. ; Aidos, H. ; 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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Abstract
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.