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International Journal of Biomedical Imaging
Volume 2015, Article ID 109804, 11 pages
http://dx.doi.org/10.1155/2015/109804
Research Article

Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network

1Department of Computer Science, Chubu University, 1200 Matsumotocho, Kasugai 487-8501, Japan
2Department of Electronics and Electrical Engineering, IIT Guwahati, Guwahati 781039, India
3Department of Computer Science, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
4Department of Gastroenterology, Aichi Medical University, 1-1 Karimata, Yazako, Nagakute 480-1195, Japan

Received 31 October 2014; Accepted 10 March 2015

Academic Editor: Richard H. Bayford

Copyright © 2015 Seiya Tsuda 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. H. Nakatani, K. Abe, A. Miyakawa, and S. Terakawa, “Three-dimensional measurement endoscope system with virtual rulers,” Journal of Biomedical Optics, vol. 12, no. 5, Article ID 051803-1, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. F. Mourgues, F. Devemay, and E. Coste-Maniere, “3D reconstruction of the operating field for image overlay in 3D-endoscopic surgery,” in Proceedings of the IEEE and ACM International Symposium on Augmented Reality (ISAR '01), pp. 191–192, New York, NY, USA. View at Publisher · View at Google Scholar
  3. T. Thormaehlen, H. Broszio, and P. N. Meier, “Three-dimensional endoscopy,” in Proceedings of the 2001 Falk Symposium, pp. 199–212, 2001.
  4. B. K. P. Horn, “Obtaining shape from shading information,” in The Psychology of Computer Vision, P. H. Winston, Ed., pp. 115–155, McGraw-Hill, 1975. View at Google Scholar · View at MathSciNet
  5. J. A. Sethian, “A fast marching level set method for monotonically advancing fronts,” Proceedings of the National Academy of Sciences of the United States of America, vol. 93, no. 4, pp. 1591–1595, 1996. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. R. Kimmel and J. A. Sethian, “Optimal algorithm for shape from shading and path planning,” Journal of Mathematical Imaging and Vision, vol. 14, no. 3, pp. 237–244, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. S. Y. Yuen, Y. Y. Tsui, and C. K. Chow, “A fast marching formulation of perspective shape from shading under frontal illumination,” Pattern Recognition Letters, vol. 28, no. 7, pp. 806–824, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Iwahori, K. Iwai, R. J. Woodham, H. Kawanaka, S. Fukui, and K. Kasugai, “Extending fast marching method under point light source illumination and perspective projection,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 1650–1653, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Ding, Y. Iwahori, T. Nakamura, L. He, R. J. Woodham, and H. Itoh, “Shape recovery of color textured object using fast marching method via self-calibration,” in Proceedings of the 2nd European Workshop on Visual Information Processing (EUVIP '10), pp. 92–96, Paris, France, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. D. R. Neog, Y. Iwahori, M. K. Bhuyan, R. J. Woodham, and K. Kasugai, “Shape from an endoscope image using extended Fast Marching Method,” in Proceedings of the 5th Indian International Conference on Artificial Intelligence (IICAI '11), pp. 1006–1015, December 2011. View at Scopus
  11. Y. Iwahori, K. Shibata, H. Kawanaka, K. Funahashi, R. J. Woodham, and Y. Adachi, “Shape from SEM image using fast marching method and intensity modification by neural network,” in Recent Advances in Knowledge-Based Paradigms and Applications, vol. 234 of Advances in Intelligent Systems and Computing, chapter 5, pp. 73–86, Springer, 2014. View at Publisher · View at Google Scholar
  12. Y. Ding, Y. Iwahori, T. Nakamura, R. J. Woodham, L. He, and H. Itoh, “Self-calibration and image rendering using RBF neural network,” in Knowledge-Based and Intelligent Information and Engineering Systems: 13th International Conference, KES 2009, Santiago, Chile, September 28–30, 2009, Proceedings, Part II, vol. 5712 of Lecture Notes in Computer Science, pp. 705–712, Springer, Berlin, Germany, 2009. View at Publisher · View at Google Scholar
  13. Y. Iwahori, R. J. Woodham, M. Ozaki, H. Tanaka, and N. Ishii, “Neural network based photometric stereo with a nearby rotational moving light source,” IEICE Transactions on Information and Systems, vol. 80, no. 9, pp. 948–957, 1997. View at Google Scholar · View at Scopus
  14. O. Vogel, M. Breuß, and J. Weickert, “A direct numerical approach to perspective shape-from-shading,” in Proceedings of the Vision, Modeling, and Visualization Conference (VMV '07), pp. 91–100, Saarbrücken, Germany, November 2007.
  15. S. H. Benton, The Hamilton-Jacobi Equation: A Global Approach, vol. 131, Academic Press, 1977. View at MathSciNet
  16. E. Prados and O. Faugeras, “A mathematical and algorithmic study of the Lambertian SFS problem for orthographic and pinhole cameras,” Tech. Rep. 5005, INRIA, 2003. View at Google Scholar
  17. E. Prados and O. D. Faugeras, “Unifying approaches and removing unrealistic assumptions in Shape from Shading: mathematics can help,” in Proceedings of the 8th European Conference on Computer Vision (ECCV '04), Prague, Czech Republic, May 2004.
  18. E. Prados and O. Faugeras, “Shape from shading: a well-posed problem?” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 2, pp. 870–877, June 2005. View at Publisher · View at Google Scholar
  19. Y. Shimasaki, Y. Iwahori, D. R. Neog, R. J. Woodham, and M. K. Bhuyan, “Generating lambertian image with uniform reflectance for endoscope image,” in PRoceedings of the International Workshop on Advanced Image Technology (IWAIT '13), 1C-2 (Computer Vision 1), pp. 60–65, Nagoya, Japan, January 2013.