Application of Empirical Mode Decomposition (EMD) on DaTSCAN SPECT images to explore Parkinson Disease

Parkinsonism is the second most common neurodegenerative disorder. It includes several pathologies with similar symptoms, what makes the diagnosis really difficult. I-ioflupane allows to obtain in vivo images of the brain that can be used to assist the PS diagnosis and provides a way to improve its accuracy. In this paper a new method for brain SPECT image feature extraction is shown. This novel Computer Aided Diagnosis (CAD) system is based on the Empirical Mode Decomposition (EMD), which decomposes any non-linear and non-stationary time series into a small number of oscillatory Intrinsic Mode Functions (IMF) a monotonous Residuum. A 80-DaTSCAN image database from the “Virgen de las Nieves” Hospital in Granada (Spain) was used to evaluate this method, yielding up to 95% accuracy, which greatly improves the baseline Voxel-As-Feature (VAF) approach.

► Procedure to classify DaTSCAN images using Empirical Mode Decomposition (EMD).
► Intensity normalization and filtering, PCA and SVM are used in combination with EMD.
► This approach outperforms the voxel-as-features method used as a baseline.
► Acc, Sen and Spe are highly improved (above 90%) and fairly stable on final tests.