On the empirical mode decomposition applied to the analysis of brain SPECT images

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 support vector machine (SVM) classification from the transformed EMD feature space. In particular, the combination of frequency components of the EMD transformation are found to retain regional differences in functional activity which is characteristic of AD. In general, 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% maximum accuracy and 93.52 ± 4.92% on average, with a acceptable biased estimate of the cross-validation (CV) true error, in separating AD and normal controls on this SPECT database. In this way, we achieve the “gold standard” labeling outperforming recently proposed CAD systems.

► An automatic procedure to assist the diagnosis of early Alzheimer’s disease is presented.
► It is based on feature selection using the empirical mode decomposition.
► SVM has been employed for pattern recognition and classification.
► Maximum and averaged Acc, Sen and Spe values were obtained over a wide range of operating conditions.
► IMF space provides higher discrimination ability for the Computer-Aided Diagnosis system.