Image Processing Based Automated Glaucoma Detection Techniques and Role of De-Noising: A Technical Survey

  • Sima Sahu
  • Harsh Vikram Singh
  • Basant Kumar
  • Amit Kumar Singh
  • Prabhat Kumar


This chapter presents a detailed study of the image processing steps to identify glaucoma including the key role of the de-noising in the detection of Glaucoma. De-noising plays an important role in the area of medical imaging. One of the major applications of image processing is detection of retinal diseases. Further, important diagnostic parameters to detect glaucoma are discussed in detail. Several techniques with different diagnostic parameters are used to detect glaucoma. Image acquisition is the first step in this detection process. Existing noise in the medical image may degrade the accuracy of the detection. Therefore a preprocessing step is highly required before the commencement of actual processing. In general, optical coherence tomography (OCT) and Ultrasound retinal image are corrupted by speckle noise. The speckle noise removal techniques are reviewed. The popular de-speckling approaches are classified into different groups and a brief overview is provided. The application of these de-noising methods outperforms in diagnosing the progression of glaucoma.


Glaucoma OCT image Ultrasound image De-speckling Diagnostic parameters 



This chapter is a part of my Ph.D. thesis which has been submitted to AKTU, Lucknow.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sima Sahu
    • 1
  • Harsh Vikram Singh
    • 2
  • Basant Kumar
    • 3
  • Amit Kumar Singh
    • 4
  • Prabhat Kumar
    • 4
  1. 1.Dr. A. P. J. Abdul Kalam Technical UniversityLucknowIndia
  2. 2.Department of ElectronicsKamla Nehru Institute of Technology (KNIT)SultanpurIndia
  3. 3.Department of ECEMotilal Nehru NIT, AllahabadPrayagrajIndia
  4. 4.Department of Computer Science and EngineeringNIT PatnaPatnaIndia

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