Classification of Fundus Images based on Multifractal Features

This paper presents a new approach for the classification of fundus images based on multifractal analysis. The first step of the proposed method consists to remove image background in order to reduce computation time. The next step is the multifractal analysis which consists to compute the multifractal spectrum of each image using the generalized fractal dimensions and Legendre spectrum. Then, five multifractal features are extracted from the multifractal spectrum, in parallel, GLCM characteristics and entropy are extracted from each image. Finally, ten extracted features are fed to three classifiers (SVM, KNN and DT). The proposed approach was tested on a mixed database containing 1778 images including 816 normal images and 962 abnormal images, where abnormal cases are a set of thirty nine pathologies. The SVM classifier gives the best results with a sensitivity of 94.85%.