Knowledge based framework for localization of retinal landmarks from diabetic retinopathy (DR) images

Abstract

We propose an algorithm for the detection of retinal landmarks (optic nerve head or optic disc, macula, and vasculature) based on optic cup location and anatomical structural details from diabetic retinopathy (DR) images of both left and right eye. Our algorithm uses color fundus images obtained from mydriatic camera. The algorithm proceeds through four main steps 1. Color image pre-processing- to enhance and remove noise from the image. 2. Detection of optic nerve head-The optic nerve head is located using a fact that the optic cup is the brightest region in the optic nerve head. At the same time exudates (DR lesion) which appear in same gray level as optic nerve head are suppressed since we only concentrate on optic cup and optic nerve head. So by calculating the mean value of the intensities of 50 ? 50 subimages (50 ? 50 is approximate area of optic cup) throughout the image and then selecting that 50 ? 50 sub image with the highest mean value, locates optic cup in the image. Using this, optic nerve head is located by increasing the area of interest around the optic cup. Since we detect optic cup first, which is embedded in optic nerve head there will not be any false detection of optic nerve head when size and shape of the exudates (DR lesion) are same as that of optic nerve head 3.Detection of macula-It is located at a distance of approximately twice the diameter of the optic nerve head just below the horizontal axis of the optic nerve head. 4. Detection of vasculature-We have used logical AND operation on two images, one being a thresholded image and another being an edge detected image. The thresholding is done on an adaptive histogram equalized image. Edge detection is done using canny edge detector. Proposed algorithm has been tested on both normal and DR images. Detected optic disc area is validated by comparing it with expert ophthalmologists' hand-drawn ground-truths. The quantitative performance is evaluated by calculating sensitivity, specificity and predictive value. Overall sensitivity (Se), specificity (Sp) and predictive value (PV) obtained in detecting optic nerve head from normal images and from abnormal images are 97.2%, 99.72%, and 88.75% and 93.93%, 99.72%, and 84.18% respectively.