Authors:
João Duarte
;
Helena Aidos
and
Ana Fred
Affiliation:
Instituto Superior Técnico, Portugal
Keyword(s):
Computer-Aided Diagnosis, Image Classification, Image segmentation, Alzheimer’s Disease.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Clustering
;
Feature Selection and Extraction
;
Medical Imaging
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
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.
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