Shortest Path with Backtracking Based Automatic Layer Segmentation in Pathological Retinal Optical Coherence Tomography Optical coherence tomography (OCT) is one of the most prevalent techniques for ophthalmic diagnosis. Retinal layer segmentation is very crucial for doctors to diagnose and study retinal diseases. However, manual segmentation is often a timeconsuming and subjective process. A number of methods have been proposed for layer segmentation on retinal OCT images, but these methods are not suit for retinal pathological OCT images. In this work, we propose a new method for layers (two layers, inner limiting membrane, outer segmentsretinal pigment epithelium) segmentation in pathological retinal OCT images using shortest path algorithm enhanced with backtracking and direction consistency. To quantitate the performance of the proposed method, we compared method to three segmentation methods. The experimental result shows that our method is more suited for retinal OCT images in pathological and achieves better result than the stateoftheart methods.

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images. Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.