Learning-based approach to segment pigment signs in fundus images for Retinitis Pigmentosa analysis

The automatic segmentation of variations in fundus images is being increasingly developed to establish retinal health, and to diagnose and monitor retinal abnormality. Fundus images can be acquired by low-cost and easy access diagnostic systems also in resource limited countries. In this paper, we focus on the segmentation of pigment signs in retinal fundus images, which is an important step in the diagnosis and monitoring of Retinitis Pigmentosa. At present, most of the existing approaches adopted for this purpose are based on the analysis of Optical Coherence Tomography (OCT) data, with only a few algorithms working on fundus images. The contribution of this paper is twofold. First, we propose a supervised segmentation technique for pigment sign detection in fundus images, which exploits the ensemble classifiers for both bagged and boosted decision trees, namely Random Forests and AdaBoost.M1. The ensemble classifiers are trained on feature vectors encoding the information of a set of regions obtained by partitioning a pre-processed image, so that each region is labeled as either normal fundus or pigment sign. Secondly, we have collected a large dataset of retinal images, in which the pigment signs have been segmented manually and we are making this publicly available to the research community. The performance of the classifiers has been evaluated on the proposed dataset and can be considered as a baseline for comparison. Our results demonstrate the high effectiveness of a machine learning approach as a suitable tool for automated Retinitis Pigmentosa analysis.