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Automatic Identification of Intraretinal Cystoid Regions in Optical Coherence Tomography

  • Joaquim de Moura
  • Jorge NovoEmail author
  • José Rouco
  • Manuel G. Penedo
  • Marcos Ortega
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)

Abstract

Optical Coherence Tomography (OCT) is, nowadays, one of the most referred ophthalmological imaging techniques. OCT imaging offers a window to the eye fundus in a non-invasive way, permitting the inspection of the retinal layers in a cross sectional visualization. For that reason, OCT images are frequently used in the analysis of relevant diseases such as hypertension or diabetes. Among other pathological structures, a correct identification of cystoid regions is a crucial task to achieve an adequate clinical analysis and characterization, as in the case of the analysis of the exudative macular disease.

This paper proposes a new methodology for the automatic identification of intraretinal cystoid fluid regions in OCT images. Firstly, the method identifies the Inner Limitant Membrane (ILM) and Retinal Pigment Epithelium (RPE) layers that delimit the region of interest where the intraretinal cystoid regions are placed. Inside these limits, the method analyzes windows of a given size and determine the hypothetical presence of cysts. For that purpose, a large and heterogeneous set of features were defined to characterize the analyzed regions including intensity and texture-based features. These features serve as input for representative classifiers that were included in the analysis.

The proposed methodology was tested using a set of 50 OCT images. 502 and 539 samples of regions with and without the presence of cysts were selected from the images, respectively. The best results were provided by the LDC classifier that, using a window size of \(61 \times 61\) and 40 features, achieved satisfactory results with an accuracy of 0.9461.

Keywords

Computer-aided diagnosis Retinal imaging Optical Coherence Tomography Intraretinal cystoid regions 

Notes

Acknowledgments

This work is supported by the Instituto de Salud Carlos III, Government of Spain and FEDER funds of the European Union through the PI14/02161 and the DTS15/00153 research projects and by the Ministerio de Economía y Competitividad, Government of Spain through the DPI2015-69948-R research project.

References

  1. 1.
    Novo, J., Penedo, M.G., Santos, J.: Optic disc segmentation by means of GA-optimized topological active nets. In: Campilho, A., Kamel, M. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 807–816. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-69812-8_80 CrossRefGoogle Scholar
  2. 2.
    Moura, J., Novo, J., Ortega, M., Charlón, P.: 3D Retinal vessel tree segmentation and reconstruction with OCT images. In: Campilho, A., Karray, F. (eds.) ICIAR 2016. LNCS, vol. 9730, pp. 716–726. Springer, Cham (2016). doi: 10.1007/978-3-319-41501-7_80 CrossRefGoogle Scholar
  3. 3.
    Geitzenauer, W., Hitzenberger, C.K., Schmidt-Erfurth, U.M.: Retinal optical coherence tomography: past, present and future perspectives. Br. J. Ophthalmol. 95(2), 171–177 (2011)CrossRefGoogle Scholar
  4. 4.
    Bogunovic, H., Abramoff, M.D., Zhang, L., Sonka, M.: Prediction of treatment response from retinal OCT in patients with exudative age-related macular degeneration. In: Ophthalmic Medical Image Analysis Workshop, MICCAI 2014, pp. 129–136 (2014)Google Scholar
  5. 5.
    Wilkins, G.R., Houghton, O.M., Oldenburg, A.L.: Automated segmentation of intraretinal cystoid fluid in optical coherence tomography. IEEE Trans. Biomed. Eng. 59(4), 1109–1114 (2012)CrossRefGoogle Scholar
  6. 6.
    Roychowdhury, S., Koozekanani, D.D., Radwan, S., Parhi, K.K.: Automated localization of cysts in diabetic macular edema using optical coherence tomography images. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1426–1429 (2013)Google Scholar
  7. 7.
    Wieclawek, W.: Automatic cysts detection in optical coherence tomography images. International Conference on Mixed Design of Integrated Circuits and Systems, pp. 79–82 (2015)Google Scholar
  8. 8.
    González, A., Remeseiro, B., Ortega, M., Penedo, M.G., Charlón, P.: Automatic cyst detection in OCT retinal images combining region flooding and texture analysis. IEEE International Symposium on Computer-Based Medical Systems, pp. 397–400 (2013)Google Scholar
  9. 9.
    Esmaeili, M., Dehnavi, A.M., Rabbani, H., Hajizadeh, F.: Three-dimensional segmentation of retinal cysts from spectral-domain optical coherence tomography images by the use of three-dimensional curvelet based K-SVD. J. Med. Signals Sens. 6(3), 166–171 (2016)Google Scholar
  10. 10.
    Wang, J., Zhang, M., Pechauer, A.D., Liu, L., Hwang, T.S., Wilson, D., Li, D.J., Jia, Y.: Automated volumetric segmentation of retinal fluid on optical coherence tomography. Biomed. Opt. Express 7(4), 1577–1589 (2016)CrossRefGoogle Scholar
  11. 11.
    Xu, X., Lee, K., Zhang, L., Sonka, M., Abràmoff, M.D.: Stratified sampling voxel classification for segmentation of intraretinal and subretinal fluid in longitudinal clinical OCT data. IEEE Trans. Med. Imaging 34(7), 1616–1623 (2015)CrossRefGoogle Scholar
  12. 12.
    Lang, A., Carass, A., Swingle, E.K., Al-Louzi, O., Bhargava, P., Saidha, S., Ying, H.S., Calabresi, P.A., Prince, J.L.: Automatic segmentation of microcystic macular edema in OCT. Biomed. Opt. Express 6(1), 155–169 (2014)CrossRefGoogle Scholar
  13. 13.
    Chiu, S.J., Li, X.T., Nicholas, P., Toth, C.A., Izatt, J.A., Farsiu, S.: Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation. Opt. Expr. 10(10), 19413–19428 (2010)CrossRefGoogle Scholar
  14. 14.
    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, CVPR 2005, pp. 886–893 (2005)Google Scholar
  16. 16.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971 (2002)CrossRefzbMATHGoogle Scholar
  17. 17.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Joaquim de Moura
    • 1
  • Jorge Novo
    • 1
    Email author
  • José Rouco
    • 1
  • Manuel G. Penedo
    • 1
  • Marcos Ortega
    • 1
  1. 1.Department of Computer ScienceUniversity of A CoruñaA CoruñaSpain

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