Publication

Machine Learning Based Analysis of FDG-PET Image Data for the Diagnosis of Neurodegenerative Diseases

van Veen, R., Talavera Martinez, L., Kogan, R. V., Meles, S., Mudali, D., Roerdink, J. B. T. M., Massa, F., Grazzini, M., Obeso, J. A., Rodriguez-Oroz, M. C., Leenders, K., Renken, R., de Vries, J. J. G. & Biehl, M., 2018, Volume 310: Applications of Intelligent Systems. Petkov, N., Strisciuglio, N. & Travieso-González, C. (eds.). Vol. 310. p. 280-289 10 p. (Frontiers in Artificial Intelligence and Applications).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Alzheimer's disease (AD) and Parkinson's disease (PD) are two common, progressive neurodegenerative brain disorders. Their diagnosis is very challenging at an early disease stage, if based on clinical symptoms only. Brain imaging techniques such as [18F]-fluoro-deoxyglucose positron emission tomography (FDG-PET) can provide important additional information with respect to changes in the cerebral glucose metabolism. In this study, we use machine learning techniques to perform an automated classification of FDG-PET data. The approach is based on the extraction of features by applying the scaled subprofile model with principal component analysis (SSM/PCA) in order to extract characteristics patterns of glucose metabolism. These features are then used for discriminating healthy controls, PD and AD patients by means of two machine learning frameworks: Generalized Matrix Learning Vector Quantization (GMLVQ) with local and global relevance matrices, and Support Vector Machines (SVMs) with a linear kernel. Datasets from different neuroimaging centers are considered. Results obtained for the individual centers, show that reliable classification is possible. We demonstrate, however, that cross-center classification can be problematic due to potential center-specific characteristics of the available FDG-PET data.
Original languageEnglish
Title of host publicationVolume 310: Applications of Intelligent Systems
EditorsN. Petkov, N. Strisciuglio, C. Travieso-González
Pages280-289
Number of pages10
Volume310
ISBN (Electronic)978-1-61499-929-4
Publication statusPublished - 2018

Publication series

NameFrontiers in Artificial Intelligence and Applications

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