Statistical features selection from intrinsic mode functions for pathologies detection in retina digital images

The empirical mode decomposition (EMD) is employed to analyze retina digital images in the frequency domain and statistical features are extracted from high frequency components of the analyzed images. The purpose is to classify normal versus abnormal images. Three different pathologies are considered including, circinates, drusens, and microaneurysms (MA). The most informative and non redundant features are ranked and selected by use of statistical features selection techniques; namely t-statistic, entropy, Battacharrayia statistic, the area between the receiver operating characteristic (ROC) and principal component analysis (PCA). Finally, support vector machines (SVM) with polynomial and radial basis function (RBF) kernels are used to classify retina digital images based on the selected features. The simulation results from leave-one-out method (LOOM) show the effectiveness of the EMD-Battacharrayia-SVM achieves 96.54%±0.0293 correct classification rate. Thus, features selection helps improving the accuracy of our system designed for pathologies detection in retina.