Sparse representation for optic disk detection

Automatic detection of optic disc (OD) is a crucial step in automated eye image processing. In this paper we present a novel method that utilizes the fact that ODs exhibit similar characteristics like circular shape and serves as the region where blood vessels and nerves converge. A dictionary with training patterns, built out of sub-images, with OD at the center, is utilized. For a given test image, the method examines all subimages of the pre-determined size, and expresses them as linear combinations of basis images in the dictionary, minimizing l₁-norm of the solution. The underlying assumption is that all sub-images with OD at the center, lie on a single sub-space. The proposed method was evaluated on the publicly-available database DIARETDB1 and DRIVE, with disjoint dictionary and testing images. Of the 89 images, the OD-center was detected within 2% of normalized error in 88 images. On DRIVE images we obtain accuracy of 85%.