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Optic disc detection in retinal fundus images using gravitational law-based edge detection

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Abstract

Diabetic retinopathy is one of the primary causes of vision loss worldwide. Early detection of the condition is critical for providing adequate treatment of this ailment to prevent vision loss. This detection is achieved by processing retinal fundus images. A key step in detecting diabetic retinopathy is identifying the optic disc in these images. The optic disc is similar in color and contrast to the exudates that indicate diabetic retinopathy. Hence, the optic disc has to be removed from the fundus image before exudates can be detected. Detecting the optic disc is also required in algorithms used for blood vessel segmentation in fundus images. Therefore, there is a need for approaches that accurately and quickly detect optic disc. This paper proposes a simple, deterministic, and time-efficient approach for optic disc detection by adapting an edge detection algorithm inspired by the gravitational law. Our method introduces novel pre- and post-detection steps that aim to increase the accuracy of the adapted detection method. In addition, a candidate selection technique is proposed to decrease the number of missed optic discs. The proposed methodology was found to have a detection rate of 100, 97.75, 92.90, and 95 % for DRIVE, DiaRet, DMED, and STARE datasets, respectively, which is comparatively better than existing optic disc detection schemes. Experimental results showed an average running time of 0.40 s per image, which is significantly lower than available methods published in the literature.

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Authors and Affiliations

  1. Computer Engineering Department, College of Computing Sciences and Engineering, Kuwait University, Kuwait City, Kuwait

    Mohammad Alshayeji, Suood Abdulaziz Al-Roomi & Sa’ed Abed

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  1. Mohammad Alshayeji
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  2. Suood Abdulaziz Al-Roomi
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  3. Sa’ed Abed
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Correspondence to Mohammad Alshayeji.

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Alshayeji, M., Al-Roomi, S.A. & Abed, S. Optic disc detection in retinal fundus images using gravitational law-based edge detection. Med Biol Eng Comput 55, 935–948 (2017). https://doi.org/10.1007/s11517-016-1563-0

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  • DOI: https://doi.org/10.1007/s11517-016-1563-0

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