Classification of neurodegenerative dementia by Gaussian Mixture Models applied to SPECT images
2012-10, Stühler, Elisabeth, Platsch, Günther, Weih, Markus, Kornhuber, Johannes, Kuwert, Torsten, Merhof, Dorit
Gaussian mixture (GM) models can be applied for statistical classification of various types of dementia. As opposed to linear boundaries, they do not only provide the class membership of a case, but also a measure of its probability. This enables an improved interpretation and classification of neurodegenerative dementia datasets which comprise various stages of the disease, and also mixed forms of dementia. In this work, GM models are applied to a total number of 103 technetium-99methylcysteinatedimer (99mTc-ECD) SPECT datasets of asymptomatic controls (CTR), as well as Alzheimer’s disease (AD) and frontotemporal dementia (FTD) patients in early or moderate stages of the disease. Prior to classification, multivariate analysis is applied: Principal component analysis (PCA) is used for dimensionality reduction, followed by a differentiation of the datasets via multiple discriminant analysis (MDA). A GM model on the resulting discrimination plane is constructed by computing the GM distribution associated with the underlying training set. The posterior probabilities of each case indicate its class membership probability. The performance of GM models for classification is assessed by bootstrap resampling and cross validation. Accuracy and robustness of the method are evaluated for different numbers of principal components (PCs), and furthermore the detection rate of dementia in early stages is calculated. The GM model outperfomes classification with linear boundaries in both predicted accuracy and detection rate of early dementia, and is equally robust.
Multiple Discriminant Analysis of SPECT Data for Alzheimer’s Disease, Frontotemporal Dementia and Asymptomatic Controls
2011-10, Stühler, Elisabeth, Platsch, Günther, Weih, Markus, Kornhuber, Johannes, Kuwert, Torsten, Merhof, Dorit
Multiple discriminant analysis (MDA) is a generalization of the Fisher discriminant analysis (FDA) and makes it possible to discriminate more than two classes by projecting the data onto a subspace. In this work, it was applied to technetium- 99methylcysteinatedimer (99mTc-ECD) SPECT datasets of 10 Alzheimer’s disease (AD) patients, 11 frontotemporal dementia (FTD) patients and 11 asymptomatic controls (CTR). Principal component analysis (PCA) was used for dimensionality reduction, followed by projection of the data onto a discrimination plane via MDA. In order to separate the different groups, linear boundaries were calculated by applying FDA to two classes at a time (linear machine). By executing the F-test for different numbers of principal components and examining the corresponding classification accuracy, an optimal discrimination plane based on the first three principal components was determined. In order to further assess the method, another dataset comprising patients with early-onset AD and FTD (beginning or suspected disease) was projected by the same method onto this discrimination plane, resulting in a correct classification for most cases. The successful iscrimination of another dataset on the same plane indicates that the model is well suited to account for
disease-specific characteristics within the classes, even for patients with early-onset AD and FTD.