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Related Experiment Video

Updated: Feb 13, 2026

In Vivo Dynamics of Retinal Microglial Activation During Neurodegeneration: Confocal Ophthalmoscopic Imaging and Cell Morphometry in Mouse Glaucoma
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Automatic Glaucoma Detection Method Applying a Statistical Approach to Fundus Images.

Anindita Septiarini1, Dyna M Khairina1, Awang H Kridalaksana1

  • 1Department of Computer Science, Faculty of Computer Science and Information Technology, Mulawarman University, Samarinda, Indonesia.

Healthcare Informatics Research
|March 6, 2018
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Summary

This study introduces a new glaucoma detection method using statistical features from optic nerve head images and the k-nearest neighbor algorithm. The approach achieved 95.24% accuracy, offering a promising tool for diagnosing this leading cause of blindness.

Keywords:
ClassificationFundusGlaucomaOptic NeuropathyRetinal Degeneration

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • Glaucoma is a leading cause of irreversible blindness globally.
  • The prevalence of glaucoma is projected to increase.
  • Early detection of glaucoma is crucial for managing the disease.

Purpose of the Study:

  • To propose a novel method for glaucoma detection.
  • To utilize statistical features from optic nerve head images.
  • To employ the k-nearest neighbor algorithm for classification.

Main Methods:

  • Extraction of three statistical features: mean, smoothness, and 3rd moment from optic nerve head images.
  • Feature selection using the correlation feature selection method.
  • Classification of features using the k-nearest neighbor (KNN) algorithm for glaucoma detection.

Main Results:

  • The proposed method was evaluated on 84 fundus images (41 glaucoma, 43 normal).
  • The system achieved a high accuracy of 95.24% in distinguishing between glaucoma and normal cases.
  • The selected statistical features proved effective for automated detection.

Conclusions:

  • The developed method demonstrates strong performance for glaucoma detection.
  • The combination of statistical features and KNN offers a viable approach for computer-aided diagnosis of glaucoma.
  • This technique shows potential for improving early detection rates of glaucoma.