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Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques

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Abstract

Glaucoma is a chronic and irreversible neuro-degenerative disease in which the neuro-retinal nerve that connects the eye to the brain (optic nerve) is progressively damaged and patients suffer from vision loss and blindness. The timely detection and treatment of glaucoma is very crucial to save patient’s vision. Computer aided diagnostic systems are used for automated detection of glaucoma that calculate cup to disc ratio from colored retinal images. In this article, we present a novel method for early and accurate detection of glaucoma. The proposed system consists of preprocessing, optic disc segmentation, extraction of features from optic disc region of interest and classification for detection of glaucoma. The main novelty of the proposed method lies in the formation of a feature vector which consists of spatial and spectral features along with cup to disc ratio, rim to disc ratio and modeling of a novel mediods based classier for accurate detection of glaucoma. The performance of the proposed system is tested using publicly available fundus image databases along with one locally gathered database. Experimental results using a variety of publicly available and local databases demonstrate the superiority of the proposed approach as compared to the competitors.

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Acknowledgments

This research is funded by National ICT R&D fund, Pakistan. We are also thankful to armed forces institute of ophthalmology (AFIO) for their clinical support and help.

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Authors and Affiliations

  1. Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences & Technology, Rawalpindi, Pakistan

    M. Usman Akram, Anam Tariq, M. Younus Javed & Sarmad Abbas

  2. Computer Engineering Department, Bahria University, Islamabad, Pakistan

    Shehzad Khalid

  3. Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan

    Ubaid Ullah Yasin

Authors
  1. M. Usman Akram
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  2. Anam Tariq
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  3. Shehzad Khalid
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  4. M. Younus Javed
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  5. Sarmad Abbas
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  6. Ubaid Ullah Yasin
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Correspondence to M. Usman Akram.

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Akram, M.U., Tariq, A., Khalid, S. et al. Glaucoma detection using novel optic disc localization, hybrid feature set and classification techniques. Australas Phys Eng Sci Med 38, 643–655 (2015). https://doi.org/10.1007/s13246-015-0377-y

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  • DOI: https://doi.org/10.1007/s13246-015-0377-y

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