REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey.

Robust optic disc and cup segmentation with deep learning for glaucoma detection

Computer-Aided diagnosis of glaucoma using fundus images: A review

Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma

Classification of glaucoma using hybrid features with machine learning approaches

Early diagnosis of diseases related with retina such as glaucoma is of utmost importance in current scenario as it is the second most prevailing cause of irreversible blindness over the world and is expected to increase further in near future. It is commonly diagnosed using retinal images which are acquired by digital fundus cameras. But the acquired images may be prone to certain outliers that create hindrance in diagnosis of glaucoma by tempering the accuracy. These outliers include retinal vessels, low contrast of images and uneven illumination that deteriorates the performance of disc and cup segmentation which are the key indicators to diagnose glaucoma. Thus, pre-processing of retinal images to remove outliers plays a significant role in diagnosis. This paper presents an approach for pre-processing the retinal fundus image followed by its comparison with state of the art. Based on the experimental analysis the performance of the proposed approach is found to be better than the state of the art based on the analysis using metrics such as peak signal to ratio, mean square error and structural similarity index. Further, the proposed approach has been compared with state of the art using metrics such as Jaccard index and dice similarity on the basis of segmentation outcomes on different pre-processing approaches.

Pre-processing of Retinal Images for Removal of Outliers

Glaucoma is an eye disease which causes continuous increase in size of optic cup and finally the permanent vision loss due to damage to the optic nerve. It is the second most prevailing disease all over the world which causes irreversible vision loss or blindness. It is caused due to increased pressure in the eyes which enlarges size of optic cup and further blocks flow of fluid to the optic nerve and deteriorates the vision. Cup to disc ratio is the measure indicator used to detect glaucoma. It is the ratio of sizes of optic cup to disc. The aim of this analysis is to study the performance of various segmentation approaches used for optic cup and optic disc so far by different researchers for detection of glaucoma in time.

Niharika Thakur

Papers

Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma

Classification of glaucoma using hybrid features with machine learning approaches

Pre-processing of Retinal Images for Removal of Outliers

Comparative Analysis on Optic Cup and Optic Disc Segmentation for Glaucoma Diagnosis