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An adaptive threshold based image processing technique for improved glaucoma detection and classification.

Ashish Issac1, M Partha Sarathi1, Malay Kishore Dutta1

  • 1Department of Electronics & Communication Engineering, Amity University, Noida, India.

Computer Methods and Programs in Biomedicine
|September 1, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method using digital fundus images for glaucoma detection. The approach accurately identifies glaucoma by analyzing key features like cup-to-disc ratio, achieving high diagnostic accuracy.

Keywords:
Adaptive thresholdCup to disc ratioFundus imageGlaucomaOptic cupOptic disc

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Glaucoma is a leading cause of irreversible blindness globally, stemming from optic neuropathy.
  • Early and accurate detection of glaucoma is crucial for preventing vision loss.
  • Digital fundus imaging is a primary tool for examining the optic nerve head.

Purpose of the Study:

  • To develop an automatic image processing method for glaucoma detection using digital fundus images.
  • To utilize discriminatory features of glaucoma, including cup-to-disc ratio (CDR) and neuroretinal rim (NRR) area, for improved diagnostic accuracy.
  • To enhance the robustness of glaucoma detection against variations in image quality and noise.

Main Methods:

  • An automatic image processing technique was developed for glaucoma detection.
  • Key features such as cup-to-disc ratio (CDR), neuroretinal rim (NRR) area, and blood vessel characteristics were extracted.
  • Segmentation of the optic disc and cup was performed using an adaptive thresholding method based on local pixel intensities, ensuring image quality invariance.

Main Results:

  • The proposed method achieved a high accuracy of 94.11% and a sensitivity of 100% in glaucoma classification.
  • Extracted features demonstrated higher significance compared to traditional statistical or textural features used in prior studies.
  • The adaptive thresholding approach proved effective in segmenting optic disc and cup, irrespective of image quality or noise.

Conclusions:

  • The developed method offers a clinically significant improvement in the accuracy of glaucoma classification from digital fundus images.
  • The use of specific discriminatory features and adaptive segmentation enhances the reliability and applicability of the diagnostic tool.
  • This automated approach holds promise for wider clinical acceptance in glaucoma screening and diagnosis.