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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Corneal Endothelium Image Segmentation Using Feedforward Neural Network

DOI: http://dx.doi.org/10.15439/2017F54

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 629637 ()

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Abstract. In this paper the problem of corneal endothelium image segmentation is considered. Particularly, a fully automatic approach for delineating contours of corneal endothelial cells is proposed. The approach produces one pixel width outline of cells. It bases on a simple feedforward neural network trained to recognize pixels which belong to the cell borders. The edge probability (edginess) map output by the network is next analysed row by row and column by column in order to find local peaks of the network response. These peaks are considered as cell border candidates and in the last step of the method via binary morphological processing are linked to create continuous outlines of cells. The results of applying the proposed approach to publicity available data set of corneal endothelium images as well as the assessment of the method against ground truth segmentation are presented and discussed. Obtained results show, that the proposed approach performs very well. The resulting mean absolute error of cell number determination is around 5\% while the average DICE measure reaches 0.83 which is a good result, especially when one pixel width objects are compared.


  1. M. Ko, J. Lee, and J. Chi, “Cell density of the corneal endothelium in human fetus by flat preparation,” Cornea, vol. 19, no. 1, pp. 80–83, 2000. http://dx.doi.org/10.1097/00003226-200001000-00016
  2. W. Bourne, “Biology of the corneal endothelium in health and disease,” Eye, vol. 17, no. 8, pp. 912–918, 2003. http://dx.doi.org/10.1038/sj.eye.6700559
  3. S. Jonuscheit, M. J. Doughty, and K. Ramaesh, “In vivo confocal microscopy of the corneal endothelium: comparison of three morphometry methods after corneal transplantation,” Eye, vol. 25, no. 9, pp. 1130–1137, 2011. http://dx.doi.org/10.1038/eye.2011.121
  4. G. Ayala, M. Diaz, and L. Martinez-Costa, “Granulometric moments and corneal endothelium status,” Pattern Recognition, vol. 34, no. 6, pp. 1219–1227, 2001.
  5. R. Nadachi and K. Nunokawa, “Automated corneal endothelial cell analysis,” in Fifth Annual IEEE Symposium on Computer-Based Medical Systems, 1992, pp. 450–457.
  6. F. Sanchez-Marin, “Automatic segmentation of contours of corneal cells,” Computers in Biology and Medicine, vol. 29, no. 4, pp. 243–258, 1999.
  7. M. Mahzoun, K. Okazaki, H. Mitsumoto, H. Kawai, Y. Sato, S. Tamura, and K. Kani, “Detection and complement of hexagonal borders in corneal endothelial cell image,” Medical Imaging Technology, vol. 14, no. 1, pp. 56–69, 1996.
  8. K. Habrat, M. Habrat, J. Gronkowska-Serafin, and A. Piorkowski, “Cell detection in corneal endothelial images using directional filters,” in Image Processing and Communications Challenges 7, ser. Advances in Intelligent Systems and Computing. Springer, 2016, vol. 389, pp. 113–123.
  9. A. Piorkowski, K. Nurzynska, J. Gronkowska-Serafin, B. Selig, C. Boldak, and D. Reska, “Influence of applied corneal endothelium image segmentation techniques on the clinical parameters,” Comput. Med. Imag. Grap., vol. 55, pp. 13–27, 2017. http://dx.doi.org/10.1016/j.compmedimag.2016.07.010
  10. L. M. Vincent and B. R. Masters, “Morphological image processing and network analysis of cornea endothelial cell images,” pp. 212–226, 1992.
  11. B. Selig, F. Malmberg, and C. L. Luengo Hendriks, “Fast evaluation of the robust stochastic watershed,” in Mathematical Morphology and its Applications to Signal and Image Processing : Proceedings of the 12th International Syposium on Mathematical Morphology, Reykjavik, Iceland, ser. Lecture Notes in Computer Science, vol. 9082, no. 9082, 2015, pp. 705–716.
  12. J. Angulo and S. Matou, “Automatic quantification of in vitro endothelial cell networks using mathematical morphology,” in 5th IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP’05), 2005, pp. 51–56.
  13. Y. Gavet and J.-C. Pinoli, “Visual perception based automatic recognition of cell mosaics in human corneal endothelium microscopy images,” Image Analysis & Stereology, vol. 27, no. 1, pp. 53–61, 2008. http://dx.doi.org/10.5566/ias.v27.p53-61
  14. J. Bullet, T. Gaujoux, V. Borderie, I. Bloch, and L. Laroche, “A reproducible automated segmentation algorithm for corneal epithelium cell images from in vivo laser scanning confocal microscopy,” Acta Ophthalmol., vol. 92, no. 4, pp. e312–e316, 2014. http://dx.doi.org/10.1111/aos.12304
  15. K. Charlampowicz, D. Reska, and C. Boldak, “Automatic segmentation of corneal endothelial cells using active contours,” Advances In Computer Science Research, vol. 14, pp. 47–60, 2014.
  16. D. Issam and E. T. Kamal, “Waterballoons: A hybrid watershed balloon snake segmentation,” Image Vision Comput., vol. 26, no. 7, pp. 905–912, 2008. http://dx.doi.org/10.1016/j.imavis.2007.10.010
  17. F. Scarpa and A. Ruggeri, “Segmentation of corneal endothelial cells contour by means of a genetic algorithm,” in Ophthalmic Medical Image Analysis Second International Workshop, 2015, pp. 25–32.
  18. A. Piorkowski, K. Nurzynska, J. Gronkowska-Serafin, B. Selig, C. Boldak, and D. Reska, “Influence of applied corneal endothelium image segmentation techniques on the clinical parameters,” Computerized Medical Imaging and Graphics, in press.
  19. M. Foracchia and A. Ruggeri, “Cell contour detection in corneal endothelium in-vivo microscopy,” in Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143), vol. 2, 2000. http://dx.doi.org/10.1109/IEMBS.2000.897902 pp. 1033–1035.
  20. A. Ruggeri, F. Scarpa, M. De Luca, C. Meltendorf, and J. Schroeter, “A system for the automatic estimation of morphometric parameters of corneal endothelium in alizarine red-stained images,” British Journal of Ophthalmology, vol. 94, no. 5, pp. 643–647, 2010. http://dx.doi.org/10.1136/bjo.2009.166561
  21. M. Foracchia and A. Ruggeri, “Corneal endothelium cell field analysis by means of interacting bayesian shape models,” in 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007. http://dx.doi.org/10.1109/IEMBS.2007.4353724 pp. 6035–6038.
  22. E. Poletti and A. Ruggeri, Segmentation of Corneal Endothelial Cells Contour through Classification of Individual Component Signatures. Cham: Springer International Publishing, 2014, pp. 411–414. ISBN 978-3-319-00846-2
  23. F. Scarpa and A. Ruggeri., “Development of a reliable automated algorithm for the morphometric analysis of human corneal endothelium,” Cornea, vol. 35, no. 9, pp. 1222–1228, 2016. http://dx.doi.org/10.1097/ICO.0000000000000908
  24. . Laboratory of Biomedical Imaging and BioImLab, “Endothelial cell Alizarine data set,” http://bioimlab.dei.unipd.it/Endo%20Aliza%20Data%20Set.htm.
  25. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, “Multiscale vessel enhancement filtering,” ser. Lecture Notes in Computer Science, W. M. Wells, A. Colchester, and S. Delp, Eds., 1998, vol. 1496, pp. 130–137.
  26. D. W. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters,” SIAM Journal on Applied Mathematics, vol. 11, no. 2, pp. 431–441, 1963. http://dx.doi.org/10.1137/0111030