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Mathematical Biosciences and Engineering

2021, Volume 18, Issue 3: 2033-2076. doi: 10.3934/mbe.2021106
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Detection of glaucoma using retinal fundus images: A comprehensive review

  • 1.
    Department of Software Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
  • 2.
    Department of Computer Science, Government College University, Faisalabad 38000, Pakistan
  • 3.
    Department of Electrical Engineering, Mirpur University of Science and Technology (MUST), Mirpur- AJK 10250, Pakistan
  • Received: 12 December 2020 Accepted: 01 February 2021 Published: 02 March 2021

  • Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.

    Citation: Amsa Shabbir, Aqsa Rasheed, Huma Shehraz, Aliya Saleem, Bushra Zafar, Muhammad Sajid, Nouman Ali, Saadat Hanif Dar, Tehmina Shehryar. Detection of glaucoma using retinal fundus images: A comprehensive review[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2033-2076. doi: 10.3934/mbe.2021106

    Related Papers:

  • Content-based image analysis and computer vision techniques are used in various health-care systems to detect the diseases. The abnormalities in a human eye are detected through fundus images captured through a fundus camera. Among eye diseases, glaucoma is considered as the second leading case that can result in neurodegeneration illness. The inappropriate intraocular pressure within the human eye is reported as the main cause of this disease. There are no symptoms of glaucoma at earlier stages and if the disease remains unrectified then it can lead to complete blindness. The early diagnosis of glaucoma can prevent permanent loss of vision. Manual examination of human eye is a possible solution however it is dependant on human efforts. The automatic detection of glaucoma by using a combination of image processing, artificial intelligence and computer vision can help to prevent and detect this disease. In this review article, we aim to present a comprehensive review about the various types of glaucoma, causes of glaucoma, the details about the possible treatment, details about the publicly available image benchmarks, performance metrics, and various approaches based on digital image processing, computer vision, and deep learning. The review article presents a detailed study of various published research models that aim to detect glaucoma from low-level feature extraction to recent trends based on deep learning. The pros and cons of each approach are discussed in detail and tabular representations are used to summarize the results of each category. We report our findings and provide possible future research directions to detect glaucoma in conclusion.


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