Abstract
Glaucoma, an eye disease recognized to be the second most leading cause of blindness worldwide. Early detection and subsequent treatment of glaucoma is hence important as damage done by glaucoma is irreversible. Large scale manual screening of glaucoma is a challenging task as skilled manpower in ophthalmology is low. Hence many works have been done towards automated glaucoma detection system from the color fundus images (CFI). In this paper, we propose a novel method of automated glaucoma detection from CFI using color channel adaptive thresholding and ISNT rule. Structural features such as cup-to-disk ratio (CDR), neuro-retinal rim (NRR) area of the optic nerve head (ONH) are extracted from CFI using color channel adaptive thresholding and morphological processing in order to segment Optic Disk (OD) and Optic Cup (OC) required for calculating the CDR value. The results obtained by the proposed methodology are very promising yielding an overall efficiency of 99%.
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