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Selected aspects of corneal endothelial segmentation quality

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The number and shape of cells in endothelium layer is highly correlated with the quality of vision. Therefore, its precise and automatic description plays an important role in medicine. This work presents several aspects of image processing of endothelium layer acquired by specular microscope. The comparison of cell selection accuracy is discussed using two different approaches to solve this problem: convolution filtering methods, and snake-based method. Moreover, for verification results generated by dedicated software, supplied with the microscope, were utilized. Next, the precise segmentation method is applied to improve the segmentation. The results are inspected visually, but also CV, H, and CVSL parameters, used in medicine, are calculated. The research concludes that general visual outcomes achieved by all segmentation approaches give similar results, however deep insight into cell outline position reveals some differences, which were partially removed after precise segmentation application. The analysis of parameter values show high stability of CV and CVSL parameters.
Rocznik
Tom
Strony
155--163
Opis fizyczny
Bibliogr. 30 poz., rys., tab.
Twórcy
  • AGH University of Science and Technology, Departament of Geoinformatics and Applied Computer Science, Cracow, Poland
autor
  • Silesian University of Technology, Institute of Informatics, Gliwice, Poland
autor
  • Bialystok University of Technology, Bialystok, Poland
autor
  • Bialystok University of Technology, Bialystok, Poland
  • Oejenafdelingen, Regionshospitalet Holstebro, Laegaardvej 12a, 7500 Holstebro, Denmark
  • Pomeranian Medical University, Departament of Ophthalmology, Szczecin, Poland
Bibliografia
  • [1] ADELSON E. H., ANDERSON C. H., BERGEN J. R., BURT P. J., OGDEN J. M. Pyramid methods in image processing. RCA engineer, 1984, Vol. 29. pp. 33–41.
  • [2] BIELECKA M., BIELECKI A., KORKOSZ M., SKOMOROWSKI M., WOJCIECHOWSKI W., ZIELINSKI B. Application of shape description methodology to hand radiographs interpretation. Computer Vision and Graphics, 2010, Vol. 6374 of Lecture Notes in Computer Science. Springer, pp. 11–18.
  • [3] CHARLAMPOWICZ K., RESKA D., BOLDAK C. Automatic segmentation ff corneal endothelial cells using active contours. Advances In Computer Science Research, 2014, Vol. 14. pp. 47–60.
  • [4] DOUGHTY M. The ambiguous coefficient of variation: Polymegethism of the corneal endothelium and central corneal thickness. International Contact Lens Clinic, 1990, Vol. 17. pp. 240–248.
  • [5] DOUGHTY M. Concerning the symmetry of the hexagonal cells of the corneal endothelium. Experimental eye research, 1992, Vol. 55. pp. 145–154.
  • [6] FORACCHIA M., RUGGERI A. Corneal endothelium analysis by means of bayesian shape modeling. Proc. 25th Annual International Conference of the IEEE-EMBS, 2003. pp. 794–797.
  • [7] GRONKOWSKA-SERAFIN J., PIORKOWSKI A. Corneal endothelial grid structure factor based on coefficient of variation of the cell sides lengths. Image Processing and Communications Challenges 5, 2014, Vol. 233 of Advances in Intelligent Systems and Computing. Springer, pp. 13–19.
  • [8] HABRAT K. Binarization of corneal endothelial digital images. 2012.
  • [9] HABRAT K., HABRAT M., GRONKOWSKA-SERAFIN J., PIORKOWSKI A. Cell detection in corneal endothelial images using directional filters. Image Processing and Communications Challenges 7, 2016, Vol. 389 of Advances in Intelligent Systems and Computing. Springer.
  • [10] HOPPENREIJS V., PELS E., VRENSEN G., TREFFERS W. Corneal endothelium and growth factors. Survey of Ophthalmology, 1996, Vol. 41. Elsevier, pp. 155–164.
  • [11] JAWOREK-KORJAKOWSKA J., TADEUSIEWICZ R. Determination of border irregularity in dermoscopic color images of pigmented skin lesions. Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2014. pp. 6459–6462.
  • [12] KASS M., WITKIN A., TERZOPOULOS D. Snakes: Active contour models. International Journal of Computer Vision, 1988, Vol. 1. pp. 321–331.
  • [13] KHAN M. A. U., NIAZI M. K. K., KHAN M. A., IBRAHIM M. T. Endothelial cell image enhancement using non-subsampled image pyramid. Information Technology Journal, 2007, Vol. 6. pp. 1057–1062.
  • [14] KO M., LEE J., CHI J. Cell density of the corneal endothelium in human fetus by flat preparation. Cornea, 2000, Vol. 19. pp. 80–83.
  • [15] KO M., PARK W., LEE J., CHI J. A histomorphometric study of corneal endothelial cells in normal human foetuses. Exp Eye Res, 2001, Vol. 72. pp. 403–409.
  • [16] MAHZOUN M., OKAZAKI K., MITSUMOTO H., KAWAI H., SATO Y., TAMURA S., KANI K. Detection and complement of hexagonal borders in corneal endothelial cell image. Medical Imaging Technology, 1996, Vol. 14. pp. 56–69.
  • [17] NADACHI R., NUNOKAWA K. Automated corneal endothelial cell analysis. Computer-Based Medical Systems, 1992. Proceedings., Fifth Annual IEEE Symposium on, 1992. pp. 450–457.
  • [18] OBLAK E., DOUGHTY M., OBLAK L. A semi-automated assessment of cell size and shape in monolayers, with optional adjustment for the cell-cell border width-application to human corneal endothelium. Tissue and Cell, 2002, Vol. 34. pp. 283–295.
  • [19] OGIELA M., TADEUSIEWICZ R. Artificial intelligence methods in shape feature analysis of selected organs in medical images. Image Processing and Communications, 2000, Vol. 6. pp. 3–11.
  • [20] OSZUTOWSKA-MAZUREK D., MAZUREK P., SYCZ K., WAKER-WÓJCIUK G. Estimation of fractal dimension according to optical density of cell nuclei in papanicolaou smears. Information Technologies in Biomedicine, 2012. Springer, pp. 456–463.
  • [21] PIORKOWSKI A., GRONKOWSKA-SERAFIN J. Selected issues of corneal endothelial image segmentation. Journal of Medical Informatics & Technologies, 2011, Vol. 17. pp. 239–245.
  • [22] PIORKOWSKI A., GRONKOWSKA-SERAFIN J. Towards precise segmentation of corneal endothelial cells. Bioinformatics and Biomedical Engineering, 2015, Vol. 9043 of Lecture Notes in Computer Science. Springer International Publishing, pp. 240–249.
  • [23] PIORKOWSKI A., MAZUREK P., GRONKOWSKA-SERAFIN J. Comparison of assessment regularity methods dedicated to isotropic cells structures analysis. Image Processing and Communications Challenges 6, 2015, Vol. 313 of Advances in Intelligent Systems and Computing. Springer, pp. 169–178.
  • [24] PLACZEK B. Rough sets in identification of cellular automata for medical image processing. Journal of Medical Informatics and Technologies (ISSN: 1642-6037), 2013, Vol. 22. pp. 161–168.
  • [25] POLETTI E., RUGGERI A. Segmentation of corneal endothelial cells contour through classification of individual component signatures. XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013, 2014. pp. 411–414.
  • [26] RESKA D., JURCZUK K., BOLDAK C., KRETOWSKI M. Mesa: Complete approach for design and evaluation of segmentation methods using real and simulated tomographic images. Biocybernetics and Biomedical Engineering, 2014, Vol. 34. pp. 146–158.
  • [27] SAEED K., TABĘDZKI M., RYBNIK M., ADAMSKI M. K3M: A universal algorithm for image skeletonization and a review of thinning techniques. International Journal of Applied Mathematics and Computer Science, 2010, Vol. 20. pp. 317–335.
  • [28] SANCHEZ-MARIN F. Automatic segmentation of contours of corneal cells. Computers in Biology and Medicine, 1999, Vol. 29. pp. 243–258.
  • [29] SELIG B., VERMEER K. A., RIEGER B., HILLENAAR T., HENDRIKS C. L. L. Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy. BMC Medical Imaging, 2015, Vol. 15. BioMed Central Ltd, p. 13.
  • [30] SZOSTEK K., GRONKOWSKA-SERAFIN J., PIORKOWSKI A. Problems of corneal endothelial image binarization. Schedae Informaticae, 2011, Vol. 20. pp. 211–218.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f05c5ec0-3be3-491c-a243-d2f8844feb2c
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