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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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Detection of Glaucoma Using Image Processing Techniques: A Critique.

B Naveen Kumar1, R P Chauhan1, Nidhi Dahiya1

  • 1a School of Biomedical Engineering , National Institute of Technology , Kurukshetra , India.

Seminars in Ophthalmology
|December 9, 2016
PubMed
Summary

Early detection of glaucoma, a leading cause of blindness, is crucial. This review summarizes image processing techniques for automated glaucoma detection from retinal images, aiding in timely diagnosis and prevention.

Keywords:
Bayes and SVM classifierCDR and ISNT ratioK-means clusteringPCAglaucoma

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Science

Background:

  • Glaucoma is a progressive optic nerve disease causing vision loss, often due to elevated intraocular pressure.
  • Early glaucoma stages are asymptomatic, highlighting the need for effective early detection methods.
  • Timely intervention is critical to prevent irreversible vision impairment and blindness.

Purpose of the Study:

  • To review and summarize various image processing techniques for automated glaucoma detection.
  • To provide an overview of methods used in analyzing retinal fundus images for glaucoma diagnosis.
  • To compare different approaches for enhancing the accuracy and efficiency of glaucoma screening.

Main Methods:

  • Utilizing retinal fundus images for automated analysis.
  • Applying pre-processing techniques to enhance image quality and features.
  • Employing diverse classification algorithms for glaucoma detection.

Main Results:

  • Automated analysis of retinal images offers a more efficient and potentially more accurate alternative to manual methods.
  • Different image processing techniques and classifiers yield varying degrees of success in glaucoma detection.
  • The review identifies advantages and disadvantages of the discussed methods.

Conclusions:

  • The reviewed image processing techniques offer promising avenues for automated glaucoma detection.
  • Selecting the optimal technique depends on specific clinical needs and performance metrics.
  • Further research can refine these methods for improved diagnostic accuracy and patient outcomes.