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Computational Methods for Exudates Detection and Macular Edema Estimation in Retinal Images: A Survey

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Abstract

Automated retinal image analysis has been widey adopted for diagnosing ophthalmic and systemic diseases which includes macular edema, diabetic and hypertensive retinopathy. Automatic assessment of retinal images can assist clinicians in screening patients with early diagnosis and in turn provide timely treatment to prevent vision loss. The appearance of exudates in retinal images is one of the early symptom of macular edema and diabetic retinopathy. This paper presents a review of image analysis/computer vision techniques utilized for exudate detection and segmentation in retinal images. The objectives of this paper are to categorize different techniques for exudate detection and provide a critical analysis on the effectiveness of each technique. Besides comparative analysis a detailed overview of quantifiable performance measures of reviewed techniques is also presented. A anatomical structures and disease manifestation in retinal images are also provided for new researchers. We have also presented a summary of publicly available datasets for exudata detection. Moreover, the current trends, open problems and future research direction in automated screening of macular edema has been discussed.

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Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, National University of Sciences and Technology, H-12, Islamabad, Pakistan

    M. M. Fraz, M. Badar & A. W. Malik

  2. Faculty of Science Engineering and Computing, Kingston University, London, KT12EE, UK

    S. A. Barman

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  1. M. M. Fraz
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Fraz, M.M., Badar, M., Malik, A.W. et al. Computational Methods for Exudates Detection and Macular Edema Estimation in Retinal Images: A Survey. Arch Computat Methods Eng 26, 1193–1220 (2019). https://doi.org/10.1007/s11831-018-9281-4

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