Positron emission tomography (PET) provides a functional imaging modality to detect
signs of dementias in human brains. Two-dimensional empirical mode decomposition (2D-EMD)
provides means to analyze such images. It decomposes the latter into characteristic modes which
represent textures on different spatial scales. These textures provide informative features for subsequent classification
purposes. The study proposes a new EMD variant which relies on a Green's function based estimation method including a
tension parameter to fast and reliably estimate the envelope hypersurfaces interpolating extremal points of the twodimensional
intensity distrubution of the images. The new method represents a fast and stable bi-dimensional EMD which
speeds up computations roughly 100-fold. In combination with proper classifiers these exploratory feature extraction
techniques can form a computer aided diagnosis (CAD) system to assist clinicians in identifying various diseases from
functional images alone. PET images of subjects suffering from Alzheimer's disease are taken to illustrate this ability.
Keywords: Alzheimer’s disease, empirical mode decomosition, Green’s function, SVM, RF.
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