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Decision Support System for Detection of Papilledema through Fundus Retinal Images

  • Image & Signal Processing
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Abstract

A condition in which the optic nerve inside the eye is swelled due to increased intracranial pressure is known as papilledema. The abnormalities due to papilledema such as opacification of Retinal Nerve Fiber Layer (RNFL), dilated optic disc capillaries, blurred disc margins, absence of venous pulsations, elevation of optic disc, obscuration of optic disc vessels, dilation of optic disc veins, optic disc splinter hemorrhages, cotton wool spots and hard exudates may result in complete vision loss. The ophthalmologists detect papilledema by means of an ophthalmoscope, Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and ultrasound. Rapid development of computer aided diagnostic systems has revolutionized the world. There is a need to develop such type of system that automatically detects the papilledema. In this paper, an automated system is presented that detects and grades the papilledema through analysis of fundus retinal images. The proposed system extracts 23 features from which six textural features are extracted from Gray-Level Co-occurrence Matrix (GLCM), eight features from optic disc margin obscuration, three color based features and seven vascular features are extracted. A feature vector consisting of these features is used for classification of normal and papilledema images using Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. The variations in retinal blood vessels, color properties, texture deviation of optic disc and its peripapillary region, and fluctuation of obscured disc margin are effectively identified and used by the proposed system for the detection and grading of papilledema. A dataset of 160 fundus retinal images is used which is taken from publicly available STARE database and local dataset collected from Armed Forces Institute of Ophthalmology (AFIO) Pakistan. The proposed system shows an average accuracy of 92.86% for classification of papilledema and normal images. It also shows an average accuracy of 97.85% for classification of already classified papilledema images into mild and severe papilledema. The proposed system is a novel step towards automated detection and grading of papilledema. The results showed that the technique is reliable and can be used as clinical decision support system.

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Acknowledgements

This research is being conducted under BIOMISA (www.biomisa.org) research group. The dataset is taken from Armed forces institute of ophthalmology (AFIO), Rawalpindi, Pakistan. The ophthalmologists from AFIO helped in annotation and validation of data.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS Institute of Information Technology, Wah Cantonment, Pakistan

    Shahzad Akbar & Muhammad Sharif

  2. Department of Computer Engineering, College of E&ME, National University of Sciences and Technology, Islamabad, Pakistan

    Muhammad Usman Akram & Anam Tariq

  3. Armed Forces Institute of Ophthalmology, Rawalpindi, Pakistan

    Ubaid ullah Yasin

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  1. Shahzad Akbar
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Correspondence to Shahzad Akbar.

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Akbar, S., Akram, M.U., Sharif, M. et al. Decision Support System for Detection of Papilledema through Fundus Retinal Images. J Med Syst 41, 66 (2017). https://doi.org/10.1007/s10916-017-0712-9

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