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A Hybrid Entropy Based Method Using Gaussian Kernel for Retinal Blood Vessel Segmentation

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Book cover Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 940))

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Abstract

Extraction of blood vessel from the retina is a major task for detecting/diagnosing eye related diseases such as diabetes, glaucoma etc. Manual segmentation is a difficult task and can be made easier by developing automated segmentation algorithms. This paper presents a quick survey of retinal segmentation methods. An entropy based optimal thresholded and length filtered image obtained from the matched filter response of Gaussian probability distribution function kernel on the enhanced (by contrast limited adaptive histogram equalization) green channel image is proposed and it gives better result when compared to other works in the literature.

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Acknowledgments

The authors would like to thank Central University of Kerala for providing support for carrying out this research work.

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Authors and Affiliations

  1. Department of Computer Science, Central University of Kerala, Periya, 671316, Kerala, India

    N. K. Adhish, R. Rajesh & T. M. Thasleema

Authors
  1. N. K. Adhish
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  2. R. Rajesh
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  3. T. M. Thasleema
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Corresponding author

Correspondence to R. Rajesh .

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Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Adhish, N.K., Rajesh, R., Thasleema, T.M. (2020). A Hybrid Entropy Based Method Using Gaussian Kernel for Retinal Blood Vessel Segmentation. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_25

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