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Bulletin of the Polish Academy of Sciences Technical Sciences

The Journal of Polish Academy of Sciences

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2300-1917
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Volume 65, Issue 1 (Feb 2017)

Issues

Denoising methods for improving automatic segmentation in OCT images of human eye

A. Stankiewicz
  • Division of Signal Processing and Electronic Systems, Poznan University of Technology, 24 Jana Pawla II St., 60-965 Poznan, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ T. Marciniak
  • Corresponding author
  • Division of Signal Processing and Electronic Systems, Poznan University of Technology, 24 Jana Pawla II St., 60-965 Poznan, Poland
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ A. Dąbrowski
  • Division of Signal Processing and Electronic Systems, Poznan University of Technology, 24 Jana Pawla II St., 60-965 Poznan, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ M. Stopa
  • Clinical Eye Unit and Pediatric Ophthalmology Service, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 16/18 Grunwaldzka St., 60-780 Poznan, Poland Poland
  • Department of Optometry and Biology of Visual System, Poznan University of Medical Sciences, 5D Rokietnicka St., 60-806 Poznan, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ P. Rakowicz
  • Clinical Eye Unit and Pediatric Ophthalmology Service, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 16/18 Grunwaldzka St., 60-780 Poznan, Poland Poland
  • Department of Optometry and Biology of Visual System, Poznan University of Medical Sciences, 5D Rokietnicka St., 60-806 Poznan, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ E. Marciniak
  • Clinical Eye Unit and Pediatric Ophthalmology Service, Heliodor Swiecicki University Hospital, Poznan University of Medical Sciences, 16/18 Grunwaldzka St., 60-780 Poznan, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-02-17 | DOI: https://doi.org/10.1515/bpasts-2017-0009

Abstract

This paper presents analysis of selected noise reduction methods used in optical coherence tomography (OCT) retina images (the socalled B-scans). The tested algorithms include median and averaging filtering, anisotropic diffusion, soft wavelet thresholding, and multiframe wavelet thresholding. Precision of the denoising process was evaluated based on the results of automated retina layers segmentation, since this stage (vital for ophthalmic diagnosis) is strongly dependent on the image quality. Experiments were conducted with a set of 3D low quality scans obtained from 10 healthy patients and 10 patients with vitreoretinal pathologies. Influence of each method on the automatic image segmentation for both groups of patients is thoroughly described. Manual annotations of investigated retina layers provided by ophthalmology experts served as reference data for evaluation of the segmentation algorithm.

Keywords: optical coherence tomography (OCT); image denoising; image segmentation; anisotropic diffusion; wavelet thresholding

References

  • [1] T. Kudasik and S. Miechowicz, “Methods of reconstructing complex multi-structural anatomical objects with RP techniques”, Bull. Pol. Ac.: Tech. 64 (2), 315-323 (2016).Web of ScienceGoogle Scholar

  • [2] T. Kudasik, M. Libura, O. Markowska, and S. Miechowicz, “Methods for designing and fabrication large-size medical models for orthopaedics”, Bull. Pol. Ac.: Tech. 63 (3), 623-627 (2015).Web of ScienceGoogle Scholar

  • [3] M. Wojtkowski, “High-speed optical coherence tomography: basics and applications”, Appl. Opt. 49 (16), D30-D61 (2010).CrossrefGoogle Scholar

  • [4] M.D. Abràmoff, M.K. Garvin, and M. Sonka, “Retinal imaging and image analysis”, IEEE Reviews in Biomedical Engineering 3, 169-208 (2010).Google Scholar

  • [5] Optovue Inc., RTVue XR 100 Avanti System. User manual. Software Version 2016.0.0, 2016.Google Scholar

  • [6] M.F. Kraus, B. Potsaid, et al., “Motion correction in optical coherence tomography volumes on a per A-scan basis using orthogonal scan patterns”, Biomedical Optics Express 3 (6), 1182-1199 (2012).CrossrefWeb of ScienceGoogle Scholar

  • [7] B. Karamata, K. Hassler, M. Laubscher, and T. Lasser, “Speckle statistics in optical coherence tomography”, J. Opt. Soc. Am. A 22 (4), 593-596 (2005).CrossrefGoogle Scholar

  • [8] M.A. Mayer, A. Borsdorf, et al., “Wavelet denoising of multiframe optical coherence tomography data”, Biomedical Optics Express 3 (3), 572-589 (2012).CrossrefGoogle Scholar

  • [9] A. Baghaie, R.M. D’souza, and Z. Yu, “Sparse and low rank decomposition based batch image alignment for speckle reduction of retinal OCT images”, IEEE 12th International Symposium on Biomedical Imaging, (2015).Google Scholar

  • [10] J. Rogowska, “Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images”, Physics in Medicine and Biology 47 (4), 641-655 (2002).Google Scholar

  • [11] D.L. Marks, T.S. Ralston, and S.A. Boppart, “Speckle reduction by I-divergence regularization in optical coherence tomography”, J. Opt. Soc. Am. A 22 (11), 2366-2371 (2005).CrossrefGoogle Scholar

  • [12] A. Wong, A. Mishra, K. Bizheva, and D.A. Clausi, “General Bayesian estimation for speckle noise reduction in optical coherence tomography retinal imagery”, Opt. Express 18 (8), 8338-8352 (2010).Web of ScienceCrossrefGoogle Scholar

  • [13] R. Bernardes, C. Maduro, et al., “Improved adaptive complex diffusion despeckling filter”, Opt. Express 18 (23), 24048-24059 (2010).CrossrefWeb of ScienceGoogle Scholar

  • [14] P. Puvanathasan and K. Bizheva, “Interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in optical coherence tomography images”, Opt. Express 17 (2), 733-746 (2009).Web of ScienceCrossrefGoogle Scholar

  • [15] W. Habib, A.M. Siddiqui, and I. Touqir, “Wavelet based despeckling of multiframe optical coherence tomography data using similarity measure and anisotropic diffusion filtering”, IEEE International Conference on Bioinformatics and Biomedicine, 330-333 (2013).Google Scholar

  • [16] Z. Hongwei, L. Baowang, and F. Juan, “Adaptive wavelet transformation for speckle reduction in optical coherence tomography images”, IEEE International Conference onSignal Processing, Communications and Computing, 1-5 (2011).Google Scholar

  • [17] S. Chitchian, M.A. Fiddy, and N.M. Fried, “Denoising during optical coherence tomography of the prostate nerves via wavelet shrinkage using dual-tree complex wavelet transform”, J. Biomedical Optics 14 (1), 14-31 (2009).Web of ScienceGoogle Scholar

  • [18] Z. Jian, L. Yu, B. Rao, B.J. Tromberg, and Z. Chen, “Three-dimensional speckle suppression in optical coherence tomography based on the curvelet transform”, Optics Express 18 (2), 1024-1032 (2010).CrossrefWeb of ScienceGoogle Scholar

  • [19] A. Ozcan, A. Bilenca, A.E. Desjardins, B.E. Bouma, and G.J. Tearney, “Speckle reduction in optical coherence tomography images using digital filtering”, J. Opt. Soc. Am. A 24 (7), 1901-1910 (2007).CrossrefGoogle Scholar

  • [20] L. Wang, Z. Meng, et al., “Adaptive speckle reduction in OCT volume data based on block-matching and 3-D filtering”, IEEE Phot. Technol. Lett. 24 (20), 1802-1804 (2012).Google Scholar

  • [21] J.J. Gómez-Valverde, J.E. Ortuño, et al., “Evaluation of speckle reduction with denoising filtering in optical coherence tomography for dermatology”, IEEE 12th International Symposium on Biomedical Imaging, (2015).Google Scholar

  • [22] K.S. Abbirame, N. Padmasini, R. Umamaheshwari, and S.M. Yacin, “Speckle noise reduction in spectral domain optical coherence tomography retinal images using fuzzification method”, Int. Conf. on Green Computing Communication and Electrical Engineering, 1-6 (2014).Google Scholar

  • [23] S.J. Chiu, X.T. Li, et al., “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation”, Optics express 18 (18), 19413-19428 (2010).CrossrefWeb of ScienceGoogle Scholar

  • [24] P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion”, IEEE Trans. Pattern Anal. and Mach. Intell. 12, 629-639 (1990).CrossrefGoogle Scholar

  • [25] S. Mallat, A Wavelet Tour of Signals Processing, 3rd ed., Academic Press, 2009.Google Scholar

  • [26] G. Nason and B. Silverman, “The stationary wavelet transform and some statistical applications”, Lecture Notes in Statistics 103, 281-299 (1995).Google Scholar

  • [27] M. Sonka and M.D. Abràmoff, “Quantitative analysis of retinal OCT”, Medical Image Analysis 33, 165-169 (2016).Web of ScienceGoogle Scholar

  • [28] A. Stankiewicz, T. Marciniak, A. Dabrowski, M. Stopa, and E. Marciniak, “A new OCT-based method to generate virtual maps of vitreomacular interface pathologies”, Proceedings of 18th IEEE International Conference on Signal Processing Algorithms, Architectures, Arrangements, and Applications, 83-88 (2014).Google Scholar

  • [29] A. Stankiewicz, T. Marciniak, et al., “Improving segmentation of 3D retina layers based on graph theory approach for low quality OCT images”, Metrology and Measurement Systems 23 (2), 269-280 (2015).Google Scholar

  • [30] Mathworks Inc., Matlab R2014b. User’s Guide, 2014.Google Scholar

About the article

Published Online: 2017-02-17

Published in Print: 2017-02-01


Citation Information: Bulletin of the Polish Academy of Sciences Technical Sciences, ISSN (Online) 2300-1917, DOI: https://doi.org/10.1515/bpasts-2017-0009.

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© Bulletin of the Polish Academy of Sciences. Technical Sciences. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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