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Filtering and Segmentation of Retinal OCT Images

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Book cover Computer Aided Systems Theory – EUROCAST 2017 (EUROCAST 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10672))

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Abstract

This work presents a method for the segmentation of optical coherence tomography images of the retina. Before segmenting the tomography, anisotropic diffusion is applied to reduce noise, but preserve the relevant edges. Afterward, the intensity profile of the images is analyzed to extract an initial approximation for the segmentation of three bands within the retina. Finally, a combination of attraction and regularization terms is used to refine the segmentation by fitting the limits of the bands to the highest gradients and smoothing their shapes to make them more regular. From the bands extracted in the different slices of the tomography, a three-dimensional reconstruction is performed for a better visualization of the results.

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References

  1. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)

    Article  Google Scholar 

  2. Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)

    Article  MATH  Google Scholar 

  3. Alemán-Flores, M., Álvarez-León, L., Caselles, V.: Texture-oriented anisotropic filtering and geodesic active contours in breast tumor ultrasound segmentation. J. Math. Imaging Vis. 28(1), 81–97 (2007)

    Article  MathSciNet  Google Scholar 

  4. Alvarez, L., Baumela, L., Márquez-Neila, P., Henríquez, P.: A real time morphological snakes algorithm. Image Process. On Line 2, 1–7 (2012)

    Article  Google Scholar 

  5. Alvarez, L., Baumela, L., Henríquez, P., Márquez-Neila, P.: Morphological snakes. In: Proceedings CVPR, pp. 2197–2202 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Departamento de Informática y Sistemas, Universidad de Las Palmas de Gran Canaria, Las Palmas, Spain

    Miguel Alemán-Flores

  2. Departamento de Morfología, Universidad de Las Palmas de Gran Canaria, Las Palmas, Spain

    Rafael Alemán-Flores

Authors
  1. Miguel Alemán-Flores
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  2. Rafael Alemán-Flores
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Corresponding author

Correspondence to Miguel Alemán-Flores .

Editor information

Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

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Cite this paper

Alemán-Flores, M., Alemán-Flores, R. (2018). Filtering and Segmentation of Retinal OCT Images. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10672. Springer, Cham. https://doi.org/10.1007/978-3-319-74727-9_34

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  • DOI: https://doi.org/10.1007/978-3-319-74727-9_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74726-2

  • Online ISBN: 978-3-319-74727-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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