Table of Contents Author Guidelines Submit a Manuscript
Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 202934, 11 pages
http://dx.doi.org/10.1155/2015/202934
Research Article

A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery

1Department of Computer Science and Technology, School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2National Engineering Research Center for Information Technology in Agriculture, Beijing 100089, China
3Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received 9 June 2015; Accepted 8 November 2015

Academic Editor: Chuangyin Dang

Copyright © 2015 Shu Tian 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. G. Brian and H. Taylor, “Cataract blindness—challenges for the 21st century,” Bulletin of the World Health Organization, vol. 79, no. 3, pp. 249–256, 2001. View at Google Scholar · View at Scopus
  2. D. Su, Z. Yang, Q. Li et al., “Identification and functional analysis of GJA8 mutation in a chinese family with autosomal dominant perinuclear cataracts,” PLoS ONE, vol. 8, no. 3, Article ID e59926, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. World Health Organization, The World Health Report: Life in the 21st Century—A Vision for All, World Health Organization, Geneva, Switzerland, 1998, http://www.who.int/whr/1998/en/whr98_en.pdf.
  4. World Health Organization, Magnitude and causes of visual impairments, 2002, http://www.who.int/mediacentre/factsheets/fs282/en/index.html.
  5. A. Coleman and J. Morrison, Management of Cataracts and Glaucoma, Taylor & Francis, Oxfordshire, UK, 2005.
  6. Z.-B. Wang, H.-W. Hao, X.-C. Yin, and Q. Liu, “An intelligent recognition system for the phacoemulsification cataract surgery,” in Proceedings of the Chinese Conference on Pattern Recognition (CCPR ’09), pp. 741–745, Nanjing, China, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. S. Kurapkiene, R. Raitelaitiene, A. Paunksnis et al., “The relationship of ultrasonic and mechanical properties of human nuclear cataract: a pilot study,” Ultragarsas, vol. 54, pp. 39–43, 2005. View at Google Scholar
  8. B. E. K. Klein, R. Klein, K. Lee Pedula Linton, Y. L. Magli, and M. W. Neider, “Assessment of cataracts from photographs in the Beaver Dam Eye Study,” Ophthalmology, vol. 97, no. 11, pp. 1428–1433, 1990. View at Publisher · View at Google Scholar · View at Scopus
  9. C. Martinyi, C. Bahn, and R. Meyer, Slit Lamp: Examination and Photography, Time One Ink Ltd Press, Flagstaff, Ariz, USA, 2007.
  10. S. K. West, F. Rosenthal, H. S. Newland, and H. R. Taylor, “Use of photographic techniques to grade nuclear cataracts,” Investigative Ophthalmology & Visual Science, vol. 29, no. 1, pp. 73–77, 1988. View at Google Scholar · View at Scopus
  11. L. T. Chylack Jr., J. K. Wolfe, D. M. Singer et al., “The lens opacities classification system III,” Archives of Ophthalmology, vol. 111, no. 6, pp. 831–836, 1993. View at Google Scholar
  12. B. Thylefors, L. T. Chylack Jr., K. Konyama et al., “A simplified cataract grading system,” Ophthalmic Epidemiology, vol. 9, no. 2, pp. 83–95, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. D. D. Duncan, O. B. Shukla, S. K. West, and O. D. Schein, “New objective classification system for nuclear opacification,” Journal of the Optical Society of America, vol. 14, no. 6, pp. 1197–1204, 1997. View at Publisher · View at Google Scholar · View at Scopus
  14. S. Fan, C. Dyer, L. Hubbard, and B. Klein, “An automatic system for classification of nuclear sclerosis from slit-lamp photographs,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2003, vol. 2878 of Lecture Notes in Computer Science, pp. 592–601, Springer, Berlin, Germany, 2003. View at Publisher · View at Google Scholar
  15. H. Li, J. H. Lim, J. Liu et al., “A computer-aided diagnosis system of nuclear cataract,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 7, pp. 1690–1698, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. W. Huang, K. L. Chan, H. Li, J. H. Lim, J. Liu, and T. Y. Wong, “A computer assisted method for nuclear cataract grading from slit-lamp images using ranking,” IEEE Transactions on Medical Imaging, vol. 30, no. 1, pp. 94–107, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. V. Baldas, L. Tang, P. Bountris, G. Saleh, and D. Koutsouris, “A real-time automatic instrument tracking system on cataract surgery videos for dexterity assessment,” in Proceedings of the 10th International Conference on Information Technology and Applications in Biomedicine (ITAB ’10), pp. 1–4, Corfu, Greece, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-learning-detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1409–1422, 2012. View at Publisher · View at Google Scholar · View at Scopus
  19. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 1–27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. X.-C. Yin, Q. Liu, H.-W. Hao, Z.-B. Wang, and K. Huang, “FMI image based rock structure classification using classifier combination,” Neural Computing and Applications, vol. 20, no. 7, pp. 955–963, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. R. Duda and P. Hart, “Use of the hough transformation to detect lines and curves in pictures,” Communications of the ACM, vol. 15, no. 1, pp. 11–15, 1972. View at Publisher · View at Google Scholar · View at Scopus
  22. Z. Cheng and Y. Liu, “Efficient technique for ellipse detection using restricted randomized hough transform,” in Proceedings of the International Conference on Information Technology: Coding Computing, pp. 714–718, April 2004. View at Scopus
  23. T. M. Nguyen, S. Ahuja, and Q. Wu, “A real-time ellipse detection based on edge grouping,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC ’09), pp. 3280–3286, San Antonio, Tex, USA, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. L. Xu, E. Oja, and P. Kultanen, “A new curve detection method: randomized Hough transform (RHT),” Pattern Recognition Letters, vol. 11, no. 5, pp. 331–338, 1990. View at Publisher · View at Google Scholar · View at Scopus
  25. N. Kiryati, H. Kälviäinen, and S. Alaoutinen, “Randomized or probabilistic Hough transform: unified performance evaluation,” Pattern Recognition Letters, vol. 21, no. 13-14, pp. 1157–1164, 2000. View at Publisher · View at Google Scholar · View at Scopus
  26. W. Lu and J. Tan, “Detection of incomplete ellipse in images with strong noise by iterative randomized Hough transform (IRHT),” Pattern Recognition, vol. 41, no. 4, pp. 1268–1279, 2008. View at Publisher · View at Google Scholar
  27. V. Baldas, L. Tang, P. Bountris, G. Saleh, and D. Koutsouris, “A real-time automatic instrument tracking system on cataract surgery videos for dexterity assessment,” in Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB ’10), pp. 1–4, Corfu Island, Greece, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. J. Y. Bouguet, Pyramidal Implementation of the Lucas Kanade Feature Tracker, Intel Corporation, Microprocessor Research Labs, 2000.
  29. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174–188, 2002. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Avidan, “Support vector tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1064–1072, 2004. View at Publisher · View at Google Scholar · View at Scopus
  31. Z. Kalal, K. Mikolajczyk, and J. Matas, “Forward-backward error: automatic detection of tracking failures,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR ’10), pp. 2756–2759, IEEE, Istanbul, Turkey, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. Z. Kalal, J. Matas, and K. Mikolajczyk, “P-N learning: bootstrapping binary classifiers by structural constraints,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’10), pp. 49–56, IEEE, San Francisco, Calif, USA, June 2010. View at Publisher · View at Google Scholar · View at Scopus