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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...

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Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

A novel glaucomatous representation method based on Radon and wavelet transform.

Beiji Zou^1,2, Changlong Chen^1,2, Rongchang Zhao^3,4

¹School of Computer Science and Engineering, Central South University, Changsha, 410083, China.

BMC Bioinformatics

|December 26, 2019

Summary

This summary is machine-generated.

This study introduces a new method using Radon and Wavelet transforms for glaucoma detection from optic nerve head images. The approach effectively captures visual patterns, aiding in early glaucoma screening.

Keywords:

Computer-aided diagnosis Glaucoma detection Radon transform

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Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Glaucoma is an irreversible optic nerve disease.
Optic nerve head (ONH) structural changes are key indicators.
Assessing ONH from fundus images is crucial for glaucoma detection.

Purpose of the Study:

To develop an effective feature representation for ONH assessment.
To improve glaucoma detection accuracy using fundus images.

Main Methods:

Utilized Radon transform (RT) to convert fundus images into the Radon domain, capturing radial variations.
Applied discrete wavelet transform (DWT) for quantitative feature extraction and difference analysis.
Employed principal component analysis (PCA) for dimensionality reduction and support vector machine (SVM) for classification.

Main Results:

Achieved state-of-the-art glaucoma detection performance.
Reported an accuracy of 0.861 and an Area Under the Curve (AUC) of 0.906 on the RIMONE-r2 dataset.

Conclusions:

The proposed method is effective for large-scale glaucoma screening.
This approach can serve as a valuable reference for clinical glaucoma diagnosis.