An application of the empirical mode decomposition to brain magnetic resonance images classification

Abstract

A new approach to distinguish normal from abnormal brain magnetic resonance (MR) images is presented. First, the empirical mode decomposition (EMD) is applied to brain MR images to obtain high frequency intrinsic mode functions (IMF) from which features are extracted. Then, an entropy-based selection process is used to identify the most informative and non redundant features from each IMF before classification by support vector machines (SVM). The validation of the approach with a MR image database consisting of Alzheimer's disease, glioma, herpes encephalitis, metastatic bronchogenic carcinoma, multiple sclerosis, and normal condition shows its effectiveness as well as slightly better classification efficiency in comparison to using discrete wavelet transform-based alternatives. However, the EMD approach is substantially more time consuming.