Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
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MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Classification of SPECT Images of Normal Subjects versus Images of Alzheimer's Disease Patients
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hard C-means clustering for voice activity detection
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Information Sciences: an International Journal
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We present a novel classification method of SPECT images based on Gaussian mixture models (GMM) for the diagnosis of Alzheimer's disease. The aims of the model-based approach for density estimation is to automatically select regions of interest (ROIs) and to effectively reduce the dimensionality of the problem. The resulting Gaussians are constructed according to a maximum likelihood criterion employing the Expectation Maximization (EM) algorithm. By considering only the intensity levels inside the Gaussians, the resulting feature space has a significantly reduced dimensionality with respect to former approaches using the voxel intensities directly as features (VAF). With this feature extraction method one relieves the effects of 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 GMM-based method yields higher accuracy rates than the classification considering all voxel values.