Automated Detection of Optic Disc Location in Retinal Images

Abstract

This contribution presents an automated method to locate the optic disc in color fundus images. The method uses texture descriptors and a regression based method in order to determine the best circle that fits the optic disc. The best circle is chosen from a set of circles determined with an innovative\ud method, not using the Hough transform as past approaches. An evaluation of the proposed method has been done using a database of 40 images. On this data set, our method achieved 95% success rate for the localization of the optic disc and 70% success rate for the identification of the optic disc contour (as a circle)

A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images

“…The next step in our method is to detect the optic disk, and remove it from the f 5 image. Despite the fact that several methods have been proposed in the literature for detecting the optic disk (see Seo et al [19], Kande et al [20] and Lupaş cu et al [21]), we have developed our own morphological method for detecting the optic disk because of its adequacy to our present needs, but the above mentioned methods also could be adopted. Our approach to detect the optic disk relies on mathematical morphology techniques, and has two main stages, namely: (1) detection of the optic disk location; (2) detection of the optic disk boundary.…”

RIM-ONE: An open retinal image database for optic nerve evaluation

“…The second one is specialized in vessel extraction and publishes two sets of images, one for training, showing the segmentation of their corresponding vessels network, and one for testing purposes, in which two segmentations of retinal vessels can be observed. Both of them have been referenced in numerous studies [17,18].…”

A new approach to optic disc detection in human retinal images using the firefly algorithm

“…Moreover, for the pictures with less than 5 pixel of uncertainty, a success ratio of 96 % was reported for the identification of the contours of the optic disc; these results were based on tests performed on 110 images. Lupascu et al [16] identified the geometric location and contours of the optic disc. This study reported optic disc detection success rate of 95 % and an optic disc contour extraction success rate of 70 %.…”

“…Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s images. Therefore, development of automatic systems is quite important [3,8,16,19,27,29]. When retinal images are controlled automatically by the system, they can be examined and diagnosed by professional physicians when any suspicion of disease is encountered by the automatic control.…”