Table of Contents
Journal of Computational Medicine
Volume 2014, Article ID 542521, 11 pages
Research Article

Context-Based Separation of Cell Clusters for the Automatic Biocompatibility Testing of Implant Materials

Institute for Computer Science, Vision and Computational Intelligence, South Westphalia University of Applied Sciences, Frauenstuhlweg 31, 58644 Iserlohn, Germany

Received 30 September 2013; Revised 23 January 2014; Accepted 2 February 2014; Published 20 March 2014

Academic Editor: Daniel Kendoff

Copyright © 2014 S. Buhl et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Mulisch and U. Welsch, Mikroskopische Technik, Spektrum Akademischer, Heidelberg, Germany, 2010.
  2. S. Colantonio, I. Gurevich, and O. Salvetti, Automatic Fuzzy-Neural Based Segmentation of Microscopic Cell Images, Inderscience Enterprises, 2008.
  3. D. Murashov, “Two-level method for segmentation of cytological images using active contour model,” in Proceedings of the 7th International Conference on Pattern Recognition and Image Analysis (PRIA-7 '04), vol. 3, pp. 814–817, St Petersburg, Russia, 2004.
  4. G. Ramella and G. Sanniti di Baja, “Image segmentation by non-topological erosion and topological expansion,” in Advances in Mass Data Analysis of Signals and Images in Medicine Biotechnology and Chemistry, International Converences MDA, 2007. View at Google Scholar
  5. F. Sadeghian, Z. Seman, A. R. Ramli, B. H. Abdul Kahar, and M.-I. Saripan, “A framework for white blood cell segmentation in microscopic blood images using digital image processing,” Biological Procedures Online, vol. 11, no. 1, pp. 196–206, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. M. Tscherepanow, F. Zöllner, M. Hillebrand, and F. Kummert, “Automatic segmentation of unstained living cells in bright-field microscope images,” in Proceedings of the International Conference on Mass-Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry (MDA), vol. 5108, pp. 158–172, Springer, Heidelberg, Germany, 2008.
  7. F. A. Velasco and J. L. Marroquín, “Robust parametric active contours: the Sandwich Snakes,” Machine Vision and Applications, vol. 12, no. 5, pp. 238–242, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Kothari, Q. Chaudry, and M. D. Wang, “Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques,” in Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '09), pp. 795–798, Boston, Mass, USA, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. B. Nilsson and A. Heyden, “Segmentation of complex cell clusters in microscopic images: application to bone marrow samples,” Cytometry Part A, vol. 66, no. 1, pp. 24–31, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Sheehy, G. Martinez, J.-G. Frerichs, and T. Scheper, “Region and contour based cell cluster segmentation algorithm for in-situ microscopy,” in Proceedings of the 5th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE '08), pp. 168–172, Mexico City, Mexico, November 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Weixing and S. Hao, “Cell cluster image segmentation on form analysis,” in Proceedings of the 3rd International Conference on Natural Computation (ICNC '07), pp. 833–836, Haikou, China, August 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms,” IEEE Transactions on Image Processing, vol. 2, no. 2, pp. 176–201, 1993. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Haralick and L. Shapiro, Computer and Robot Vision, vol. 1, Addison-Wesley, 1992.
  14. K.-M. Lee and W. N. Street, “Model-based detection, segmentation, and classification for image analysis using on-line shape learning,” Machine Vision and Applications, vol. 13, no. 4, pp. 222–233, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. J. Cheng and J. C. Rajapakse, “Segmentation of clustered nuclei with shape markers and marking function,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 3, pp. 741–748, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Buhl, B. Neumann, and E. Eisenbarth, “Segmentation of cytological stained cell areas and generation of cell boundaries,” in Complex Shaded Cell Clusters, 55. IWK—International Scientific Colloquium, pp. 511–514, Isle Publisher, Ilmenau, Germany, 2010. View at Google Scholar
  17. M. J. Swain and D. H. Ballard, “Indexing via color histograms,” in Proceedings of the 3rd International Conference on Computer Vision, pp. 390–393, December 1990. View at Scopus
  18. P. E. Hart, N. J. Nilsson, B. Raphael et al., “Correction to a formal basis for the heuristic determination of minimum cost paths,” SIGART Newsletter, vol. 37, pp. 28–29, 1972. View at Google Scholar
  19. U. Pal, K. Rodenacker, and B. B. Chaudhuri, “Automatic cell segmentation in cyto- and histometry using dominant contour feature points,” Analytical Cellular Pathology, vol. 17, no. 4, pp. 243–250, 1998. View at Google Scholar · View at Scopus
  20. V. Metzler, H. Bienert, T. Lehmann, K. Mottaghy, and K. Spitzer, “A novel method for quantifying shape deformation applied to biocompatibility testing,” ASAIO Journal, vol. 45, no. 4, pp. 264–271, 1999. View at Google Scholar · View at Scopus