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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...

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Using Retinal Imaging to Study Dementia

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Published on: November 6, 2017

Optic disc segmentation for glaucoma screening system using fundus images.

Ahmed Almazroa^1,2, Weiwei Sun³, Sami Alodhayb⁴

¹King Abdullah International Medical Research Center (KAIMRC), Riyadh, Saudi Arabia.

Clinical Ophthalmology (Auckland, N.Z.)

|November 29, 2017

Summary

This summary is machine-generated.

This study introduces a novel optic disc segmentation algorithm using level sets and inpainting for glaucoma screening. The method improves accuracy by considering multiple ophthalmologist opinions, achieving 83.9% accuracy.

Keywords:

RIGA dataset glaucoma image inpainting image segmentation level set optic disc

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Area of Science:

Ophthalmology
Medical Image Analysis
Computer Vision

Background:

Accurate optic disc segmentation is crucial for diagnosing optic nerve head pathologies like glaucoma.
Existing methods often rely on single ophthalmologist opinions, introducing potential bias.

Purpose of the Study:

To develop a novel optic disc segmentation algorithm for automated glaucoma screening.
To improve the reliability of optic disc segmentation by incorporating inter-observer variability.

Main Methods:

A level set method applied to a localized optic disc image.
An inpainting technique to mitigate blood vessel interference.
Evaluation using the RIGA dataset and comparison with multiple ophthalmologist markings.

Main Results:

The algorithm achieved 83.9% accuracy in marking the optic disc area and centroid.
Best agreement was observed between the algorithm and manual markings in 379 out of 550 images.

Conclusions:

The proposed algorithm offers a reliable approach for optic disc segmentation in glaucoma screening.
Incorporating inter-observer variability enhances the robustness of automated diagnostic systems.