Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016, Article ID 6183218, 8 pages
http://dx.doi.org/10.1155/2016/6183218
Research Article

Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery

1Siemens Healthcare, Technology Center, Princeton, NJ 08540, USA
2Siemens Corporate Technology, 81739 Munich, Germany
3Department of Neurosurgery, Hospital Merheim, Cologne Medical Center, 51109 Cologne, Germany
4Department of Neurosurgery, Heinrich Heine University Düsseldorf, 40255 Düsseldorf, Germany

Received 26 November 2015; Revised 15 January 2016; Accepted 18 January 2016

Academic Editor: Lin Hua

Copyright © 2016 Ali Kamen 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. E. Mahe, S. Ara, M. Bishara et al., “Intraoperative pathology consultation: error, cause and impact,” Canadian Journal of Surgery, vol. 56, no. 3, pp. E13–E18, 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. P. E. Paull, B. J. Hyatt, W. Wassef, and A. H. Fischer, “Confocal laser endomicroscopy: a primer for pathologists,” Archives of Pathology & Laboratory Medicine, vol. 135, no. 10, pp. 1343–1348, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. R. C. Newton, S. V. Kemp, P. L. Shah et al., “Progress toward optical biopsy: bringing the microscope to the patient,” Lung, vol. 189, no. 2, pp. 111–119, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. G. J. Tearney, M. E. Brezinski, B. E. Bouma et al., “In vivo endoscopic optical biopsy with optical coherence tomography,” Science, vol. 276, no. 5321, pp. 2037–2039, 1997. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Charalampaki, M. Javed, S. Daali, H. Heiroth, A. Igressa, and F. Weber, “Confocal laser endomicroscopy for real-time histomorphological diagnosis,” Neurosurgery, vol. 62, pp. 171–176, 2015. View at Publisher · View at Google Scholar
  6. R. Kiesslich and M. F. Neurath, “Endoscopic confocal imaging,” Clinical Gastroenterology and Hepatology, vol. 3, no. 7, pp. S58–S60, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), pp. 886–893, IEEE, San Diego, Calif, USA, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Nister and H. Stewenius, “Scalable recognition with a vocabulary tree,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06), vol. 2, pp. 2161–2168, New York, NY, USA, June 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, Prentice-Hall, Upper Saddle River, NJ, USA, 2003.
  12. D. G. Lowe, “Object recognition from local scale-invariant features,” in Proceedings of the 7th IEEE International Conference on Computer Vision (ICCV '99), pp. 1150–1157, IEEE, September 1999. View at Scopus
  13. J. Yang, K. Yu, Y. Gong, and T. Huang, “Linear spatial pyramid matching using sparse coding for image classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '09), pp. 1794–1801, IEEE, Miami, Fla, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 3360–3367, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. L. K. Saul and S. T. Roweis, “An introduction to locally linear embedding,” http://www.cs.toronto.edu/~roweis/lle/publications.html.
  16. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, “Distributed optimization and statistical learning via the alternating direction method of multipliers,” Foundations and Trends in Machine Learning, vol. 3, no. 1, pp. 1–122, 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002. View at Publisher · View at Google Scholar · View at Scopus
  18. H. G. Feichtinger and T. Strohmer, Gabor Analysis and Algorithms: Theory and Applications, Springer, 2012.
  19. V. Jain and H. S. Seung, “Natural image denoising with convolutional networks,” in Proceedings of the 22nd Annual Conference on Neural Information Processing Systems (NIPS '08), pp. 769–776, December 2008. View at Scopus