Automatic CDR Estimation for Early Glaucoma Diagnosis

Abstract

Glaucoma is a degenerative disease that constitutes the second cause of blindness in developed countries. Although it cannot be cured, its progression can be prevented through early diagnosis. In this paper, we propose a new algorithm for automatic glaucoma diagnosis based on retinal colour images. We focus on capturing the inherent colour changes of optic disc (OD) and cup borders by computing several colour derivatives in CIE L^∗a^∗b^∗ colour space with CIE94 colour distance. In addition, we consider spatial information retaining these colour derivatives and the original CIE L^∗a^∗b^∗ values of the pixel and adding other characteristics such as its distance to the OD centre. The proposed strategy is robust due to a simple structure that does not need neither initial segmentation nor removal of the vascular tree or detection of vessel bends. The method has been extensively validated with two datasets (one public and one private), each one comprising 60 images of high variability of appearances. Achieved class-wise-averaged accuracy of 95.02% and 81.19% demonstrates that this automated approach could support physicians in the diagnosis of glaucoma in its early stage, and therefore, it could be seen as an opportunity for developing low-cost solutions for mass screening programs.

Figure 1

The OD and cup as seen on a typical retinal fundus image. The OD is presented as an almost circular region with a colour ranging from orange to yellow. The cup is the brightest region within it, with a diffuse border only distinguishable by vessel bends.

Figure 2

The OD presents a general appearance making it suitable for its automatic detection. However, there is a high variability among population: (a) clear colour change and red hue, well-defined border; (b) subtle colour change and red hue, diffuse border; (c) subtle colour change and pale yellow fuzzy border; and (d) bright yellow diffuse border presence of peripapillary atrophy.

Figure 3

Dataset1 comprises a wide range of OD and cup appearances due to their different nature, population, and acquisition devices.

Figure 4

Dataset2 is a private database compounded by retinal fundus images acquired by the same device. The overall complexity is high due to the presence of many different appearances: fuzzy edges, subtle colour changes, atrophies, and so forth.

Figure 5

Cup region segmentation is a challenging task due to its wide range of appearances: (a) bright yellow, well-defined border and small size, (b) no perceptible colour change, (c) pale yellow, well-defined border and medium size, and (d) pale yellow, diffuse border and large size.

Figure 6

Colour changes represented by gradient arrows marked in blue offer the necessary information for OD and cup segmentation.

Figure 7

Vector-based colour derivatives for three of the computed orientations: 0°, 45°, and 270°.

Figure 8

(a) Original ROI image, (b) OD probability image, and (c) cup probability image.

Figure 9

Result images for the original ROI of Figure 8. White colour corresponds to the cup, grey colour to the OD, and black colour to the background. Labels assigned by the classifier: (a) without postprocessing, (b) with AC, and (c) with AC and ellipse fitting.

Figure 10

OD and cup segmentation results. The second (automatic result without ellipse fitting) and third columns (automatic result with ellipse fitting) present the OD and the cup in green.

Figure 11

Quantities involved in CDR measurements. (a) VCDR, (b) HCDR, and (c) ACDR. Although, in (c), a rounded area is marked, accurate OD and cup borders will provide accurate ACDR results.