Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI

Abstract

Glaucoma is a leading cause of permanent blindness. However, disease progression can be limited if detected early. The optic cup-to-disc ratio (CDR) is one of the main clinical indicators of glaucoma, and is currently determined manually, limiting its potential in mass screening. In this paper, we propose an automatic CDR determination method using a variational level-set approach to segment the optic disc and cup from retinal fundus images. The method is a core component of ARGALI, a system for automated glaucoma risk assessment. Threshold analysis is used in preprocessing to estimate the initial contour. Due to the presence of retinal vasculature traversing the disc and cup boundaries which can cause inaccuracies in the detected contours, an ellipse-fitting post-processing step is also introduced. The method was tested on 104 images from the Singapore Malay Eye Study, and it was found the results produced a clinically acceptable variation of up to 0.2 CDR units from the manually graded samples, with potential use in mass screening.

Automatic extraction of retinal features from colour retinal images for glaucoma diagnosis: A review

“…Wong et al [73] proposed a level-set approach to represent the boundary of Optic Cup in the form of gradient flow equation. The gradient flow equation was initialized by a particular threshold value.…”

Optic disc segmentation using the sliding band filter

“…There are several works on the automatic segmentation of OD in retinal images which can mainly be grouped into four categories, namely template-based methods [4,5,6,7], deformable model methods [8,9,10,11,12,13], morphological-based approaches [14,15,16], and pixel classification methods [17,18]. Within the first category, Aquino et al [4] follow a voting-type algorithm to locate a pixel within the OD as initial information to define a starting sub-image.…”

“…The method proposed by Wong et al [5] uses a level-set approach to obtain the OD boundary, that is afterwards smoothed by fitting an ellipse. A general energy function proposed by Zheng et al.…”

Segmentation of Optic Disc and Optic Cup in Retinal Fundus Images Using Coupled Shape Regression

“…As a result, cup boundary is ill defined and in-homogeneous which makes the segmentation more difficult. Existing approaches of optic cup segmentation are based on level sets [8], superpixels classification [9] and sparse dictionary learning [14]. In another method [13], fusion of cup segmentations from multi-view fundus images was performed to improve the performance.…”