Detection of Anatomic Structures in Human Retinal Imagery

Abstract

The widespread availability of electronic imaging devices throughout the medical community is leading to a growing body of research on image processing and analysis to diagnose retinal disease such as diabetic retinopathy (DR). Productive computer-based screening of large, at-risk populations at low cost requires robust, automated image analysis. In this paper we present results for the automatic detection of the optic nerve and localization of the macula using digital red-free fundus photography. Our method relies on the accurate segmentation of the vasculature of the retina followed by the determination of spatial features describing the density, average thickness, and average orientation of the vasculature in relation to the position of the optic nerve. Localization of the macula follows using knowledge of the optic nerve location to detect the horizontal raphe of the retina using a geometric model of the vasculature. We report 90.4% detection performance for the optic nerve and 92.5% localization performance for the macula for red-free fundus images representing a population of 345 images corresponding to 269 patients with 18 different pathologies associated with DR and other common retinal diseases such as age-related macular degeneration.

Integrated approach for accurate localization of optic disc and macula

“…We get ξ disc (D * ) and ξ mac (M * ) values of 3.74% and 3.94%, which is considerably lower than 7% reported in [9]. On STARE dataset alone, the overall sensitivity of OD detection is 95%, comparable to reported results of [8], whereas in macula detection, our method achieves sensitivity 97.53%, an improvement over the best published results of [6]. Based on our extensive evaluation and results of the intermediate stages of our integration method, we tabulate the error percentages observed among true-positives in each dataset of our collection, along with Combined (shown in On pathological datasets such as DMED (macular edema), DIARETDB1, MESSIDOR (DR), our method shows low error for M * with nil false-positives, indicating its potential in disease grading applications.…”

“…2) by adopting a simple vessel detection step based on morphology (supremum of opening with rotated linear structuring elements). From V 0 , we iteratively estimate the axis of symmetry X i , and refine V i so that the arcade pixels are progressively selected to be symmetric about the axis From anatomy: The macula is at a distance of 2-2.5 'disc diameters' temporal to the optic disc [8], flanked by the major arcade, and is avascular. These characteristics are applied to compute macula estimate M 3 .…”

“…Some of the prominent work for the detection of macula are low intensity pixel clustering, template matching [6] and utilization of parabolic model [7] along with retinal raphe [8]. Performance of intensity based methods for macula detection are usually hindered by pathology such as GA.…”

An Automatic Screening Method to Detect Optic Disc in the Retina

“…It is often a key step for the detection of other anatomical retinal structures, such as retinal blood vessels and macula [1], [6], [7], [8]. More importantly, it helps to establish a retinal coordinate system that can be used to determine the position of other retinal abnormalities, such as exudates, drusen, and hemorrhages [9], [10].…”

An Efficient Approach for Detection of Exudates in Diabetic Retinopathy Images Using Clustering Algorithm

“…Then, the regions are grouped into a set of five clusters using K-means Clustering algorithm. By this two step process, we reduce the computational cost avoiding feature calculation for every pixel in the image [13]. The entire process can be summarized in following steps:…”