Segmentation of the optic nerve head combining pixel classification and graph search

Abstract

Early detection of glaucoma is essential to minimizing the risk of visual loss. It has been shown that a good predictor of glaucoma is the cup-to-disc ratio of the optic nerve head. This paper presents an automated method to segment the optic disc. Our approach utilizes pixel feature selection to train a feature set to recognize the region of the disc. Soft pixel classification is used to generate a probability map of the disc. A new cost function is developed for maximizing the probability of the region within the disc. The segmentation of the image is done using a novel graph search algorithm capable of detecting the border maximizing the probability of the disc. The combination of graph search and pixel classification enables us to incorporate large feature sets into the cost function design, which is critical for segmentation of the optic disc. Our results are validated against a reference standard of 82 dataseis and compared to the manual segmentations of 3 glaucoma fellows.