Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

Bulletin of the Polish Academy of Sciences Technical Sciences

The Journal of Polish Academy of Sciences

6 Issues per year


IMPACT FACTOR 2016: 1.156
5-year IMPACT FACTOR: 1.238

CiteScore 2016: 1.50

SCImago Journal Rank (SJR) 2015: 0.526
Source Normalized Impact per Paper (SNIP) 2015: 1.208

Open Access
Online
ISSN
2300-1917
See all formats and pricing
In This Section
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
/ 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:
/ A. Dąbrowski
  • Division of Signal Processing and Electronic Systems, Poznan University of Technology, 24 Jana Pawla II St., 60-965 Poznan, Poland
/ 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
/ 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
/ 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
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 Science]

  • [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 Science]

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

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

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

  • [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). [Crossref] [Web of Science]

  • [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). [Crossref]

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

  • [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).

  • [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).

  • [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). [Crossref]

  • [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 Science] [Crossref]

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

  • [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 Science] [Crossref]

  • [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).

  • [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).

  • [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 Science]

  • [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). [Crossref] [Web of Science]

  • [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). [Crossref]

  • [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).

  • [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).

  • [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).

  • [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). [Crossref] [Web of Science]

  • [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). [Crossref]

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

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

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

  • [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).

  • [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).

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

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. Export Citation

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

Comments (0)

Please log in or register to comment.
Log in