Automatic detection of hard and soft exudates from retinal fundus images

According to WHO estimates, 400 million people suffer from diabetes, and this number is likely to double by year 2030. Unfortunately, diabetes can have severe complications like glaucoma or retinopathy, which both can cause blindness. The main goal of our research is to provide an automated procedure that can detect retinopathy-related lesions of the retina from fundus images. This paper focuses on the segmentation of so-called white lesions of the retina that include hard and soft exudates. The established procedure consists of three main phases. The preprocessing step compensates the various luminosity patterns found in retinal images, using background and foreground pixel extraction and a data normalization operator similar to Z-transform. This is followed by a modified SLIC algorithm that provides homogeneous superpixels in the image. The final step is an ANN-based classification of pixels using fifteen features extracted from the neighborhood of the pixels taken from the equalized images and from the properties of the superpixel where the pixel belongs. The proposed methodology was tested using high-resolution fundus images originating from the IDRiD database. Pixelwise accuracy is characterized by a 54% Dice score in average, but the presence of exudates is detected with 94% precision.