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3D PET scans show 3D images of the cell activity in the tissues of the human brain. Having these scans, doctors can use the computer-aided diagnosis of dementia disorders like Alzheimer’s and Parkinson's. 3D PET scans are considered as high dimensional data, though not all of the layers are used during the analysis of such data, especially within classification tasks. Furthermore, the automatic classification using 3D brain images can be applied to the whole brain or using specific regions of interest (ROIs) that can be considered as structure biomarkers and relate them to particular dementia disorders. The objective of this thesis is to investigate extracting informative regions across the different 3D layers of PET scans and assess the contribution of such regions to the classification accuracy. Identifying such regions can be performed with the help of extra domain knowledge or brain parcellation methods.
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