Tracking the major temporal arcade in retinal fundus images

Accurate detection of the major temporal arcade (MTA) in retinal fundus images could assist in localization of various anatomical features of the retina, such as the optic nerve head (ONH) and fovea, as well as in detection of certain types of retinopathy. In this paper, we present a novel automated tracking algorithm to obtain a skeleton image representing only the MTA, by detection of the vascular tree using Gabor filters, detection of the center of the ONH using phase portrait analysis, and morphological image processing. The methods were trained and tested using two independent sets of 20 retinal images each. The results were evaluated in terms of the mean distance to the closest point (MDPC) computed for each tracked MTA as compared to its corresponding hand-drawn trace. The test results indicate a low average MDCP error of approximately 2 pixels per MTA skeleton. The proposed algorithm should assist in detection and diagnosis of diseases such as proliferative diabetic retinopathy and retinopathy of prematurity, as well as in localization of the ONH and fovea.