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International Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2016: Bioinformatics and Biomedical Engineering pp 380–390Cite as

Automated Detection of Diabetic Macular Edema Risk in Fundus Images

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9656))

Abstract

This paper is aimed at assessing the initial performance of a computer-based system to detect the risk of diabetic macular edema (DME). The development of this tool was funded by the Health Ministry of the Andalusian Regional Government (Spain) with the purpose of being integrated into a complete system for early diagnosis of diabetic retinopathy (DR).

The algorithmic methods are based on the detection of retinal exudates (early ophthalmic signs of DME) by fundus image processing. It has been tested on a set of 1058 macula-centred retinographies from people with diabetes at risk for retinal diseases. Each of the images was rated on a 0–2 scale (from no DME risk to moderate-severe risk) created from the observations of ophthalmologic specialists of three Andalusian Health Service Medical Centres. Since these three sets of DME expert ratings showed a high agreement and consistency, a consensus diagnosis was built and used as a ground truth. System evaluation was carried out by measuring the sensitivity and specificity of automated DME risk detection regarding this clinical reference diagnosis. In addition, system failures in real cases of DME risk (false negatives) and its clinical importance were also measured.

The system showed several promising operation points, being able to work at a sensitivity level comparable to human experts, with no clinically-important failures, and enough specificity from a hypothetical practical implementation point of view. Thus, it demonstrated 0.9039 sensitivity per image (against 0.7948, 0.9345 and 0.8690 of specialists), with all false negatives graded as mild DME risk, and 0.7696 specificity. This last value indicates that over 75 % of the images with no apparent DME risk under consideration are correctly identified by the system.

Initial performance assessment shows that the presented system for the detection of DME risk is a suitable tool to be integrated into a complete DR pre-screening tool for the automated management of patients within a screening programme. Progress in this integration is definitely associated with the need to carry out a comprehensive system evaluation.

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Acknowledgements

This work was carried out as part of the Project “Automatic System for Early Diabetic Retinopathy Detection by Retinal Digital Images Analysis”, supported and funded by the Health Ministry of the Andalusian Regional Government (Spain).

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Authors and Affiliations

  1. Department of Electronic, Computer Science and Automatic Engineering, University of Huelva, Huelva, Spain

    Diego Marin

  2. Department of Mathematics, University of Huelva, Huelva, Spain

    Manuel Emilio Gegundez-Arias

  3. North Area of Sanitary Management of Cordoba, Andalusian Health Service, Cordoba, Spain

    Carlos Ortega & Javier Garrido

  4. Virgen de Macarena University Hospital of Seville, Andalusian Health Service, Seville, Spain

    Beatriz Ponte

  5. Juan Ramon Jimenez Hospital of Huelva, Andalusian Health Service, Huelva, Spain

    Fatima Alvarez

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  1. Diego Marin
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  2. Manuel Emilio Gegundez-Arias
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  3. Carlos Ortega
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  4. Javier Garrido
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  5. Beatriz Ponte
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  6. Fatima Alvarez
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Corresponding author

Correspondence to Diego Marin .

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Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Francisco Ortuño

  2. Universidad de Granada, Granada, Spain

    Ignacio Rojas

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Marin, D., Gegundez-Arias, M.E., Ortega, C., Garrido, J., Ponte, B., Alvarez, F. (2016). Automated Detection of Diabetic Macular Edema Risk in Fundus Images. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_34

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  • DOI: https://doi.org/10.1007/978-3-319-31744-1_34

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  • Online ISBN: 978-3-319-31744-1

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