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Fusioning Multiple Treatment Retina Images into a Single One

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Neural Information Processing (ICONIP 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1333))

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Abstract

Different retina diseases require laser treatment that is applied on retina images. For example, the treatment uses laser activation applied on the retina on different points. In this case, the treatment uses only a part of the fundus image, named retina treatment image. The scope of the paper is to combine all the retina treatment images in order to map all the treatment points on the whole fundus image. Thus an annotated fundus image with all treatment points is obtained. Thus, each treatment retina image is mapped on the corresponding fundus image in order to place the spot light on it. The spot light is similar as shape with the optic disc. The differentiation between them is made based on blood vessels - optic disc contains a high density of pixels from the blood vessels. Both blood vessel segmentation and detection of the spot light and optic disc are performed using convolutional neural network. Image alignment is performed using feature matching with homography computed using GMS (grid-based motion statistics). Evaluation was performed using different fundus images selected from public datasets. From each fundus image we generated different treatment retina images - cropped and rotated parts from the original image. The laser spot was simulated through a white circle placed in different positions on the retina treatment images.

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Authors and Affiliations

  1. University Politehnica of Bucharest, 060042, Bucharest, Romania

    Irina Mocanu, Loretta Ichim & Dan Popescu

Authors
  1. Irina Mocanu
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  2. Loretta Ichim
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  3. Dan Popescu
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Corresponding author

Correspondence to Dan Popescu .

Editor information

Editors and Affiliations

  1. Department of AI, Ping An Life, Shenzhen, China

    Haiqin Yang

  2. Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Kitsuchart Pasupa

  3. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi-Sing Leung

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    James T. Kwok

  5. School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  6. The Chinese University of Hong Kong, New Territories, Hong Kong

    Irwin King

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Mocanu, I., Ichim, L., Popescu, D. (2020). Fusioning Multiple Treatment Retina Images into a Single One. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1333. Springer, Cham. https://doi.org/10.1007/978-3-030-63823-8_12

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  • DOI: https://doi.org/10.1007/978-3-030-63823-8_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63822-1

  • Online ISBN: 978-3-030-63823-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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