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Glaucoma is a progressive optic neuropathy which is a leading cause of irreversible blindness [1]. Glaucoma is described by Visual Field (VF) loss and optic nerve damage, associated with raised Intraocular Pressure (IOP) except normal-tension variant. Glaucoma can be diagnosed using different Computer Vision (CV) techniques by separating features of Retinal Fundus Images like Retinal Nerve Fibre Layer (RNFL) texture, Optic Disc (OD), Optic Cup (OC), Neuro Retinal Rim (NRR), colour intensity of OC and notch in Blood Vessels (BV) [2]. Size of OC and OD is important feature in finding of glaucoma known as “Cup to Disc Ratio (CDR)”. Here in this paper, we are using a novel approach to detect OC and OD to calculate CDR and colour intensity of cup from the images of patients from their progressive Retinal Fundus Images. We use Sequential Fundus Images for Glaucoma Forecast (SIGF) public dataset having progressive images of 300 patients. Algorithm uses computer vision techniques to analyse sequential images of a single patient to detect features with accuracy of 96.32% and it will be very useful to prevent glaucoma before it starts damaging Optic Nerves.

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Detection and Analysis of Features of Optic Nerve Head Using Retinal Fundus Images of an Eye for a Priori Prediction of Glaucoma

  • 会议论文

  • 发表日期：

    2025年

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  • Kartik Thakkar,
  • Ravi Gulati

摘要

Glaucoma is a progressive optic neuropathy which is a leading cause of irreversible blindness [1]. Glaucoma is described by Visual Field (VF) loss and optic nerve damage, associated with raised Intraocular Pressure (IOP) except normal-tension variant. Glaucoma can be diagnosed using different Computer Vision (CV) techniques by separating features of Retinal Fundus Images like Retinal Nerve Fibre Layer (RNFL) texture, Optic Disc (OD), Optic Cup (OC), Neuro Retinal Rim (NRR), colour intensity of OC and notch in Blood Vessels (BV) [2]. Size of OC and OD is important feature in finding of glaucoma known as “Cup to Disc Ratio (CDR)”. Here in this paper, we are using a novel approach to detect OC and OD to calculate CDR and colour intensity of cup from the images of patients from their progressive Retinal Fundus Images. We use Sequential Fundus Images for Glaucoma Forecast (SIGF) public dataset having progressive images of 300 patients. Algorithm uses computer vision techniques to analyse sequential images of a single patient to detect features with accuracy of 96.32% and it will be very useful to prevent glaucoma before it starts damaging Optic Nerves.