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International Journal of Biomedical Imaging
Volume 2011, Article ID 270247, 11 pages
http://dx.doi.org/10.1155/2011/270247
Research Article

Segmentation of Endothelial Cell Boundaries of Rabbit Aortic Images Using a Machine Learning Approach

1Department of Bioengineering, Imperial College London, London SW7 2AZ, UK
2Bristol Heart Institute, Bristol Royal Infirmary, Bristol BS2 8HW, UK
3Laboratoire d'InfoRmatique en Image et Systèmes d'information, Institut National des Sciences Appliquées de Lyon, LIRIS INSA De Lyon, 69621 Villeurbanne, France

Received 27 July 2010; Revised 19 March 2011; Accepted 30 March 2011

Academic Editor: Tiange Zhuang

Copyright © 2011 Saadia Iftikhar 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. S. R. Daniels, F. R. Greer, and the Committee on Nutrition, “Lipid screening and cardiovascular health in childhood,” Pediatrics, vol. 122, no. 1, pp. 198–208, 2008. View at Google Scholar
  2. B. W. McCrindle, E. M. Urbina, B. A. Dennison et al., “Drug therapy of high-risk lipid abnormalities in children and adolescents: a scientific statement from the American Heart Association Atherosclerosis, Hypertension, and Obesity in Youth Committee, Council of Cardiovascular Disease in the Young, with the Council on Cardiovascular Nursing,” Circulation, vol. 115, no. 14, pp. 1948–1967, 2007. View at Google Scholar
  3. P. F. Davies, “Flow-mediated endothelial mechanotransduction,” Physiological Reviews, vol. 75, no. 3, pp. 519–560, 1995. View at Google Scholar
  4. P. Libby, P. M. Ridker, and A. Maseri, “Inflammation and atherosclerosis,” Circulation, vol. 105, no. 9, pp. 1135–1143, 2002. View at Google Scholar
  5. A. M. Malek, S. L. Alper, and S. Izumo, “Hemodynamic shear stress and its role in atherosclerosis,” Journal of the American Medical Association, vol. 282, no. 21, pp. 2035–2042, 1999. View at Google Scholar
  6. J. T. Flaherty, J. E. Pierce, V. J. Ferrans et al., “Endothelial nuclear patterns in the canine arterial tree with particular reference to hemodynamic events,” Circulation Research, vol. 30, no. 1, pp. 23–33, 1972. View at Google Scholar
  7. J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognition, vol. 19, no. 1, pp. 41–47, 1986. View at Google Scholar
  8. S. L. Al-Musawi, J. Bishton, J. Dean et al., “Evidence for a reversal with age in the pattern of near-wall blood flow around aortic branches,” Atherosclerosis, vol. 172, no. 1, pp. 79–84, 2004. View at Google Scholar
  9. A. R. Bond, S. Iftikhar, A. A. Bharath et al., “Morphological evidence for a change in the pattern of aortic wall shear stress with age,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 31, pp. 543–550, 2011. View at Google Scholar
  10. C. J. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, pp. 121–167, 1998. View at Google Scholar
  11. E. Z. Hirsch, W. Martino, C. H. Orr et al., “A simple rapid method for the preparation of en face endothelial (Häutchen) monolayers from rat and rabbit aortas,” Atherosclerosis, vol. 37, no. 4, pp. 539–548, 1980. View at Google Scholar
  12. S. Iftikhar and A. A. Bharath, “Cell boundary analysis using radial search for dual staining techniques,” in Medical Imaging 2009: Image Processing, vol. 7259 of Proceedings of the SPIE, Lake Buena Vista, Fla, USA, February 2009.
  13. C. Kotropoulos and I. Pitas, “Segmentation of ultrasonic images using support vector machines,” Pattern Recognition Letters, vol. 24, no. 4-5, pp. 715–727, 2003. View at Google Scholar
  14. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, vol. 24, John Wiley & Sons, New York, NY, USA, 2nd edition, 2007.
  15. H. Peng, F. Long, and C. Ding, “Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp. 1226–1238, 2005. View at Google Scholar
  16. J. Shawe-Taylor and N. Cristianini, Support Vector Machines and other Kernel-Based Learning Methods, Cambridge University Press, New York, NY, USA, 1st edition, 2000.
  17. A. A. Bharath and J. Ng, “A steerable complex wavelet construction and its application to image denoising,” IEEE Transactions on Image Processing, vol. 14, no. 7, pp. 948–959, 2005. View at Google Scholar
  18. D. Glotsos, P. Spyridonos, D. Cavouras et al., “An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine,” Informatics for Health and Social Care, vol. 30, no. 3, pp. 179–193, 2005. View at Google Scholar
  19. K. K. Chin, Support Vector Machines Applied to Speech Pattern Classification, M.S. dissertation, University of Cambridge, UK, 1998.
  20. C.-C. Chang and C.-J. Lin, “Libsvm: a library for Support Vector Machines,” 2011, http://www.csie.ntu.edu.tw/~cjlin/libsvm.
  21. T. MathWorks. Matlab, Version 7.9.0 (R2009b) on PC Windows, http://www.mathworks.co.uk/.
  22. J. Canny, “A computational approach to edge detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986. View at Google Scholar
  23. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–165, 2004. View at Google Scholar
  24. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man and Cybernetic, vol. 9, no. 1, pp. 62–66, 1979. View at Google Scholar
  25. J. F. Cornhill, M. J. Levesque, E. E. Herderick et al., “Quantitative study of the rabbit aortic endothelium using vascular casts,” Atherosclerosis, vol. 35, no. 3, pp. 321–337, 1980. View at Google Scholar
  26. Y. Gavet and J. C. Pinoli, “Visual perception based automatic recognition of cell mosaics in human corneal endothelium microscopy images,” Image Analysis and Stereology, vol. 27, no. 1, pp. 53–61, 2008. View at Google Scholar
  27. S. C. Sekhar, F. Aguet, S. Romain et al., “Parametric B-spline snakes on distance maps—application to segmentation of histology images,” in Proceedings of the16th European Signal Processing Conference (EUSIPCO '08), Lausanne, Switzerland, August 2008.
  28. F. Lamberti and B. Montrucchio, “Segmentation of in-vitro endothelial cell networks,” in Proceedings of the 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro, vol. 1, pp. 129–132, April 2004.
  29. L. M. Vincent and B. R. Masters, “Morphological image processing and network analysis of cornea endothelial cell images,” in Image Algebra and Morphological Image Processing III, vol. 1769 of Proceedings of the SPIE, San Diego, Calif, USA, July 1992.
  30. C. Cortes, “Invited talk: can learning kernels help performance?” in Proceedings of the 26th Annual International Conference on Machine Learning, Association for Computing Machinery, New York, NY, USA, 2009.