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Related Concept Videos

Glaucoma: Overview01:25

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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...
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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.
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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: Feb 26, 2026

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus

U Rajendra Acharya1, Shreya Bhat2, Joel E W Koh3

  • 1Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, 599491, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia.

Computers in Biology and Medicine
|July 13, 2017
PubMed
Summary

This study introduces an automated system for glaucoma diagnosis using image analysis. It achieves 95.8% accuracy in detecting glaucoma, offering a cost-effective screening tool.

Keywords:
GRIGlaucomaHOSHistogram equalizationLM filter bankMR4MR8Optic nerveSFFSSVMSchmid filter bankTextonkNN

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Area of Science:

  • Ophthalmology
  • Computer Vision
  • Medical Image Analysis

Background:

  • Glaucoma is an optic neuropathy causing irreversible vision loss.
  • Current diagnostic methods for glaucoma are costly and require specialist interpretation.
  • Automated, cost-effective glaucoma screening is highly desirable.

Purpose of the Study:

  • To develop and evaluate a novel automated system for accurate glaucoma diagnosis.
  • To improve the efficiency and accessibility of glaucoma screening.
  • To present a new methodology for analyzing optic nerve and retinal nerve fiber layer (RNFL) images.

Main Methods:

  • Utilized adaptive histogram equalization for image conversion.
  • Applied Leung-Malik (LM), Schmid (S), and maximum response (MR4, MR8) filters for texton extraction.
  • Extracted Local Configuration Pattern (LCP) features, selected using Sequential Floating Forward Search (SFFS), and ranked via t-test.
  • Employed various classifiers, including k-nearest neighbor (kNN), for image classification.
  • Formulated a glaucoma integrative index (GRI).

Main Results:

  • Achieved a high classification accuracy of 95.8% for glaucoma detection.
  • Identified six significant features from the LM filter bank as optimal for classification.
  • Demonstrated the effectiveness of the kNN classifier in conjunction with selected features.
  • The developed system provides a reliable and effective method for glaucoma diagnosis.

Conclusions:

  • The proposed automated system offers a highly accurate and cost-effective solution for glaucoma screening.
  • The methodology effectively extracts and selects relevant features for distinguishing between normal and glaucomatous images.
  • The integration of GRI enhances the reliability of the diagnostic system.