Classification of SPECT Images Using Clustering Techniques Revisited

Abstract

We present a novel classification method of SPECT images based on clustering for the diagnosis of Alzheimer’s disease. The aims of the clustering approach which is based on Gaussian Mixture Model (GMM) for density estimation, is to automatically select Regions of Interest (ROIs) and to effectively reduce the dimensionality of the problem. The clusters represented by Gaussians are constructed according to a maximum likelihood criterion employing the expectation maximization (EM) algorithm. By considering only the intensity levels inside the clusters, the resulting feature space has a significantly reduced dimensionality with respect to former approaches using the voxel intensities directly as features. With this feature extraction method one avoids the so-called small sample size problem and nonlinear classifiers may be used to distinguish between the brain images of normal and Alzheimer patients. Our results show that for various classifiers the clustering method yields higher accuracy rates than the classification considering all voxel values.KeywordsSupport Vector MachineFeature VectorRadial Basis FunctionGaussian Mixture ModelSPECT ImageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.