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Metrology and Measurement Systems

The Journal of Committee on Metrology and Scientific Instrumentation of Polish Academy of Sciences

4 Issues per year


IMPACT FACTOR 2016: 1.598

CiteScore 2016: 1.58

SCImago Journal Rank (SJR) 2016: 0.460
Source Normalized Impact per Paper (SNIP) 2016: 1.228

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Online
ISSN
2300-1941
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Volume 23, Issue 2 (Jun 2016)

Issues

Improving Segmentation of 3D Retina Layers Based on Graph Theory Approach for Low Quality OCT Images

Agnieszka Stankiewicz / Tomasz Marciniak / Adam Dąbrowski / Marcin Stopa
  • Poznan University of Medical Sciences, Department of Optometry and Biology of Visual System, Rokietnicka 5D, 60-806 Poznań, Poland
  • Poznan University of Medical Sciences, Clinical Eye Unit and Pediatric Ophthalmology Service Heliodor Swiecicki University Hospital, Grunwaldzka 16/18, 60-780 Poznań, Poland
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Piotr Rakowicz
  • Poznan University of Medical Sciences, Department of Optometry and Biology of Visual System, Rokietnicka 5D, 60-806 Poznań, Poland
  • Poznan University of Medical Sciences, Clinical Eye Unit and Pediatric Ophthalmology Service Heliodor Swiecicki University Hospital, Grunwaldzka 16/18, 60-780 Poznań, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Elżbieta Marciniak
  • Poznan University of Medical Sciences, Clinical Eye Unit and Pediatric Ophthalmology Service Heliodor Swiecicki University Hospital, Grunwaldzka 16/18, 60-780 Poznań, Poland
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-05-31 | DOI: https://doi.org/10.1515/mms-2016-0016

Abstract

This paper presents signal processing aspects for automatic segmentation of retinal layers of the human eye. The paper draws attention to the problems that occur during the computer image processing of images obtained with the use of the Spectral Domain Optical Coherence Tomography (SD OCT). Accuracy of the retinal layer segmentation for a set of typical 3D scans with a rather low quality was shown. Some possible ways to improve quality of the final results are pointed out. The experimental studies were performed using the so-called B-scans obtained with the OCT Copernicus HR device.

Keywords: Optical Coherence Tomography (OCT); segmentation of retinal layers; image segmentation; graph theory

References

  • [1] Rogalski, A., Chrzanowski, K. (2014). Infrared Devices And Techniques (Revision). Metrol. Meas. Syst., 21(4), 565-618.Web of ScienceGoogle Scholar

  • [2] Antoniuk, P., Strąkowski, M.R., Pluciński, J., Kosmowski, B.B. (2012). Non-Destructive Inspection Of Anti- Corrosion Protective Coatings Using Optical Coherent Tomography. Metrol. Meas. Syst., 19(2), 365‒372.CrossrefWeb of ScienceGoogle Scholar

  • [3] Yaqoob, Z., Wu, J., Yang, C. (2005). Spectral domain optical coherence tomography: a better OCT imaging strategy. Biotechniques, 39 (6 Suppl), 6‒13, DOI: 10.2144/000112090.CrossrefGoogle Scholar

  • [4] SOCT Copernicus HR. (2011). User Manual Software Version 4.3.0 User Manual rev. A. Optopol.Google Scholar

  • [5] RTVue XR 100 Avanti Edition (2014). Podręcznik użytkownika. Optovue Inc.Google Scholar

  • [6] Fabritius, T., Makita, S., et al. (2009). Automated segmentation of the macula by optical coherence tomography. Opt. Express, 17(18), 15659-15669.CrossrefGoogle Scholar

  • [7] Yazdanpanah, A., Hamarneh, G., Smith, B., Sarunic, M. (2009). Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach. Proc. of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II, Springer-Verlag, 5762, 649-656.Google Scholar

  • [8] Kajic, V., Povazay, B., Hermann, B., Hofer, B., Marshall, D., Rosin, P.L., Drexler, W. (2010). Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis. Optics Express, 18(14), 14730-14744.Web of ScienceCrossrefGoogle Scholar

  • [9] Garvin, M.K., Abramoff, M.D., Kardon, R., Russell, S.R., Wu, X., Sonka, M. (2008). Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search. IEEE Transactions on Medical Imaging, 27(10), 1495-1505.CrossrefGoogle Scholar

  • [10] Chiu, S.J., Li, X.T., Nicholas, P., Toth, C.A., Izatt, J.A., Farsiu, S. (2010). Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation. Opt. Express, 18(18), 19413-19428.CrossrefWeb of ScienceGoogle Scholar

  • [11] Teng, P. (2013). Caserel ‒ An Open Source Software for Computer-aided Segmentation of Retinal Layers in Optical Coherence Tomography Images. Zenodo, DOI: 10.5281/zenodo.17893.CrossrefGoogle Scholar

  • [12] Cha, Y.M., Han, J.H. (2014). High-Accuracy Retinal Layer Segmentation for Optical Coherence Tomography Using Tracking Kernels Based on Gaussian Mixture Model. IEEE Journal of Selected Topics in Quantum Electronics, 20(2).Web of ScienceGoogle Scholar

  • [13] Szkulmowski, M., Wojtkowski, M., Sikorski, B., Bajraszewski, T., Srinivasan, V.J., Szkulmowska, A., Kaluzny, J.J., Fujimoto, J.G., Kowalczyk, A. (2007). Analysis of posterior retinal layers in spectral optical coherence tomography images of the normal retina and retinal pathologies. Journal of Biomedical Optics, 12(4).Web of ScienceGoogle Scholar

  • [14] Szkulmowski, M., Wojtkowski, M. (2013). Averaging techniques for OCT imaging. OPTICS EXPRESS, 21(8), 9757‒9773.CrossrefGoogle Scholar

  • [15] Ishikawa, H., Stein, D.M., et al. (2005). Macular segmentation with optical coherence tomography. Invest. Ophthalmol. Vis. Sci., 46(6), 2012-2017.CrossrefGoogle Scholar

  • [16] Ehnes, A., Wenner, Y., Friedburg, C., Preising, M.N., Bowl, W., Sekundo, W., Meyer zu Bexten, E., Stieger, K., Lorenz, B. (2014). Optical Coherence Tomography (OCT) Device Independent Intraretinal Layer Segmentation. Trans. Vis. Sci. Tech., 3(1).Google Scholar

  • [17] Fernandez, D.C., et al. (2005). Automated detection of retinal layer structures on optical coherence tomography images. Opt. Express, 13(25), 10200-10216.CrossrefGoogle Scholar

  • [18] Stein, D. M., et al. (2015). A New Quality Assessment Parameter for Optical Coherence Tomography. The British Journal of Ophthalmology, 90.2, 186-190.Web of ScienceCrossrefGoogle Scholar

  • [19] Dijkstra, E.W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269-271.CrossrefGoogle Scholar

  • [20] Shi, J., Malik, J. (2000). Normalized Cuts and Image Segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22(8), 888-905.CrossrefGoogle Scholar

  • [21] Stankiewicz, A., Marciniak, T., Dąbrowski, A., Stopa, M., Marciniak, E. (2014). A New OCT-based Method to Generate Virtual Maps of Vitreomacular Interface Pathologies. Proc. of SPA 2014: Signal Processing Algorithms, Architectures, Arrangements, and Applications Conference, 83‒88.Google Scholar

About the article

Received: 2015-09-30

Accepted: 2015-12-28

Published Online: 2016-05-31

Published in Print: 2016-06-01


Citation Information: Metrology and Measurement Systems, ISSN (Online) 2300-1941, DOI: https://doi.org/10.1515/mms-2016-0016.

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

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