Fully automated method for glaucoma screening using robust optic nerve head detection and unsupervised segmentation based cup-to-disc ratio computation in retinal fundus images

Abstract

Background and Objective: Visual impairment affects a significant part of the population worldwide. Glaucoma is one of these main causes, a chronic eye disease leading to progressive vision loss. Early glaucoma screening is an important task, allowing a slowing down of the pathology spreading and avoidance of irreversible vision damages. When manual assessment by experts suffers from disadvantages, exploiting the relevant Cup-to-Disc Ratio (CDR) feature as a structural indicator to assess the damage to the optic nerve head (ONH) is an efficient way for early glaucoma screening and diagnosis. Methods: In this paper, we propose a new fully automated methodology for glaucoma screening and diagnosis from retinal fundus images. In order to allow eye examination in remote locations with limited access to clinical facilities, we focus in this work on the development of a computationally-efficient algorithm for further implementation on mobile devices. First, the method provides a robust optic disc (OD) detection method, combining a brightness criterion and a template matching technique, to effectively detect the optic disc (OD) even in the presence of bright lesions associated to pathological cases. Second, an efficient optic cup (OC) and optic disc (OD) segmentation is performed, using a texture-based and model-based approach. Finally, Cup-to-Disc Ratio (CDR) computation leads to glaucoma screening with a classification between healthy and glaucomatous patients. Results: The proposed approach for glaucoma screening and diagnosis have been tested on the publicly available DRISHTI-GS1 dataset. Fifty retinal images are provided and labeled healthy or glaucomatous by trained specialists. The method achieves 98% of accuracy on final glaucoma screening and diagnosis, and excellent performance rates on evaluation metrics, outperforming the state-of-the-art CDR feature-based approaches. Conclusions: We proposed a fully automated method for glaucoma screening and diagnosis from retinal images. Excellent performance was obtained on final screening, classifying healthy and glaucomatous subjects. As it effectively detects the presence of glaucoma, in a low-computational manner, the approach can be part of a mobile help-diagnosis system, to improve the final diagnosis by the specialist and develop widespread visual health programs.

The use of deep learning technology for the detection of optic neuropathy

“…In the field of DL, there are two types of research related to the optic disc: one is related to the detection of the optic disc in the whole fundus image (19) and the other is related to the classification of the optic disc in the cropped image (20). While there has been much research on the optic disc classification algorithm, the target detection of the optic disc has not been thoroughly studied (21).…”

Fine-tuning Convolutional Neural Networks: a comprehensive guide and benchmark analysis for Glaucoma Screening

“…Diameter-based CDR calculation is then performed to lead to glaucoma assessment. In the work by Mvoulana et al [9], authors exploited a non-supervised clustering method, in combination with a model-based operator, to detect the regions of interest then automatically compute area-based CDR. Both approaches obtain excellent results on final glaucoma assessment.…”

“…First, since glaucoma disease mainly manifests itself within and around the ONH, all images are cropped around the ONH. We exploit here the method proposed in [9] to effectively detect the ONH center, and crop the retinal image around the detected center using a (224x224) or a (299x299) window (depending on the default input size required by the models). Relevant prior studies have assessed the utility to operate this cropping, improving the ability of the algorithm to feature the presence of the disease [24].…”

Detection of retinal abnormalities in fundus image using CNN deep learning networks

“…Thus, some CAD systems based on retinal analysis were developed, extracting the anatomic structures in retinal images, such as vessel segmentation [10], detecting lesions related to DR [11], diagnosing glaucoma [12,13], AMD [14] and cataract [15].…”

“…A. Mvoulana et al [13] proposed a fully automated methodology for glaucoma. Their method provides an OD detection method, combining a brightness criterion and a template matching technique, to detect the OD as a RoI and segment the OD and OD.…”