In this paper we propose a novel method for brain SPECT image feature extraction based on the Empirical Mode Decomposition (EMD). The proposed method applied to assist the diagnosis of Alzheimer Disease (AD) selects the most discriminant voxels for classification from the transformed EMD feature space. In particular, high-frequency components of the EMD transformation are found to retain regional differences in functional activity which is characteristic of AD. The EMD represents a fully data-driven, unsupervised and additive signal decomposition and does not need any a priori defined basis system. Several experiments were carried out on a balanced SPECT database collected from the “Virgen de las Nieves” Hospital in Granada (Spain), containing 96 recordings and yielding up to 100% accuracy in separating AD and normal controls, and then outperforming recently proposed CAD systems.
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