Automatic detection of microaneurysms and haemorrhages in fundus images using dynamic shape features

This paper presents a novel approach for automatic detection of microaneurysms and haemorrhages in fundus images. First, it begins with a preprocessing stage for shade correction, contrast enhancement and denoising. Second, all regional minima with sufficient contrast are extracted and considered as candidates. Third, in an image flooding scheme, a new set of dynamic shape features is computed as a function of intensity. Finally, a Random Forest classifies the candidates into lesions and non lesions. A set of 143 fundus images with an average of 2210 pixels in diameter was acquired using different cameras and used for training and testing. The proposed approach achieves a global score over the FROC curve of 0.393, while previous work with images of similar resolution reported a score of 0.233.