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Glaucoma: Overview

Glaucoma: Overview

Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...

Open Angle Glaucoma: Treatment

Open Angle Glaucoma: Treatment

In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
Drugs such as carbonic anhydrase inhibitors, α2- and...

Angle Closure Glaucoma: Treatment

Angle Closure Glaucoma: Treatment

Angle-closure glaucoma, or closed-angle glaucoma, is an eye condition where the iris bulges out and blocks the iridocorneal angle, resulting in a buildup of aqueous humor and increased intraocular pressure. Immediate medical attention is necessary due to the sudden onset of symptoms. The treatment for angle-closure glaucoma includes short-term and long-term approaches. Short-term treatment involves using eye drops like pilocarpine to lower intraocular pressure by increasing aqueous humor...

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Related Experiment Video

Updated: May 13, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

Superpixel classification based optic disc and optic cup segmentation for glaucoma screening.

Jun Cheng¹, Jiang Liu, Yanwu Xu

¹iMED Ocular Imaging Programme in Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore. jcheng@i2r.a-star.edu.sg

IEEE Transactions on Medical Imaging

|February 26, 2013

Summary

This summary is machine-generated.

Early glaucoma detection is crucial for preventing vision loss. This study introduces a novel superpixel classification method for segmenting optic discs and cups in retinal images, improving glaucoma screening accuracy.

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Glaucoma is a leading cause of irreversible vision loss.
Current screening methods relying on intraocular pressure (IOP) lack sensitivity for population-based screening.
Optic nerve head assessment in retinal fundus images offers a more promising approach for early glaucoma detection.

Purpose of the Study:

To propose and evaluate a superpixel classification method for optic disc and optic cup segmentation in retinal fundus images.
To enhance glaucoma screening by accurately measuring the cup-to-disc ratio.
To introduce a self-assessment reliability score for automated segmentation quality control.

Main Methods:

Superpixel classification utilizing histograms and center-surround statistics for optic disc segmentation.
Incorporation of location information alongside histograms and center-surround statistics for optic cup segmentation.
Evaluation on a database of 650 manually annotated retinal images.

Main Results:

Achieved average overlapping errors of 9.5% for optic disc and 24.1% for optic cup segmentation.
Demonstrated a correlation between reduced reliability scores and increased segmentation errors, validating the self-assessment.
Attained areas under the curve of 0.800 and 0.822 in two datasets for glaucoma screening, outperforming existing methods.

Conclusions:

The proposed superpixel classification method provides accurate optic disc and optic cup segmentation for glaucoma screening.
The self-assessment reliability score effectively indicates segmentation quality, aiding clinical deployment.
This automated approach shows potential for improving early glaucoma detection and patient outcomes.