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Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
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Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques.

Shishir Maheshwari1, Vivek Kanhangad1, Ram Bilas Pachori1

  • 1Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, 453552, India.

Computers in Biology and Medicine
|December 28, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces an automated glaucoma diagnosis system using bit-plane slicing and local binary patterns. The novel approach achieves 99.30% accuracy, aiding in early detection and reducing expert burden.

Keywords:
Bit-plane slicingDecision level fusionGlaucomaLocal binary patternSupport vector machine

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Glaucoma causes irreversible retinal nerve fiber damage.
  • Current diagnostic methods are manual and require expert supervision.
  • Automated systems are needed for fast, accurate mass screening of glaucoma.

Purpose of the Study:

  • To develop a novel, automated system for glaucoma diagnosis.
  • To improve the accuracy and efficiency of glaucoma screening.

Main Methods:

  • A new approach combining bit-plane slicing (BPS) and local binary patterns (LBP) for glaucoma diagnosis.
  • Color fundus images are processed by separating RGB channels, splitting them into bit planes, and extracting LBP features.
  • Features are classified using support vector machines (SVMs), with decisions fused for final classification.

Main Results:

  • The proposed automated system achieved a high accuracy of 99.30% in discriminating between normal and glaucoma cases.
  • 10-fold cross-validation confirmed the effectiveness of the BPS and LBP based approach.

Conclusions:

  • The developed system demonstrates high efficacy for glaucoma diagnosis.
  • This automated system can assist ophthalmologists in daily screening, potentially increasing diagnostic accuracy.
  • The system is ready for validation on large, diverse datasets.