Ensemble Empirical Mode Decomposition of functional Images
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This book "EMD of functional images", explores the potential of canonical two-dimensional ensemble empirical mode decomposition (2DEEMD) and Green's function in tension-based bidimensional EMD (GiTBEMD) to extract textures , so-called bidimensional intrinsic mode functions (BIMFs), of functional biomedical images, especially functional magnetic resonance images (fMRI) taken during a visual task. To identify most informative textures, i.e. BIMFs,a support vector machine (SVM) as well as a random forest (RF) classifiers are employed. Classification performance is used to estimate the discriminat...
This book "EMD of functional images", explores the potential of canonical two-dimensional ensemble empirical mode decomposition (2DEEMD) and Green's function in tension-based bidimensional EMD (GiTBEMD) to extract textures , so-called bidimensional intrinsic mode functions (BIMFs), of functional biomedical images, especially functional magnetic resonance images (fMRI) taken during a visual task. To identify most informative textures, i.e. BIMFs,a support vector machine (SVM) as well as a random forest (RF) classifiers are employed. Classification performance is used to estimate the discriminative power of extracted BIMFs. The latter are then analyzed according to their spatial distribution of brain activations related with visual task and compared with a canonical general linear model (GLM) analysis employing statistical parametric mapping (SPM). Also, a comparative study, in terms of computational costs and quality of extracted intrinsic modes, between 2DEEMD and GiTBEMD have been done based on the same collected data.
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