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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 670739, 15 pages
http://dx.doi.org/10.1155/2015/670739
Research Article

Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network

1Dr. N. G. P. Institute of Technology, Coimbatore 641048, India
2Vidhya Mandhir Institute of Technology, Tamilnadu, India

Received 10 October 2014; Revised 9 January 2015; Accepted 26 January 2015

Academic Editor: Giancarlo Ferrigno

Copyright © 2015 K. Gayathri Devi and R. Radhakrishnan. 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. American Cancer Society, “Cancer facts and figures,” 2012.
  2. P. A. Yushkevich, J. Piven, H. C. Hazlett et al., “User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability,” NeuroImage, vol. 31, no. 3, pp. 1116–1128, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. http://www.radiologyinfo.org/.
  4. http://www.invendo-medical.com/.
  5. A. Bert, I. Dmitriev, S. Agliozzo et al., “An automatic method for colon segmentation in CT colonography,” Computerized Medical Imaging and Graphics, vol. 33, no. 4, pp. 325–331, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. X. Yang, Y. Zheng, M. Siddique, and G. Beddoe, “Learning from imbalanced data: a comparative study for colon CAD,” in Medical Imaging 2008: Computer-Aided Diagnosis, vol. 6915 of Proceedings of SPIE, San Diego, Calif, USA, February 2008. View at Publisher · View at Google Scholar
  7. A. Losnegård, L. B. Hysing, L. P. Muren, E. Hodneland, and A. Lundervold, “Semi-automated segmentation of the sigmoid and descending colon for radiotherapy planning using the fast marching method,” Physics in Medicine and Biology, vol. 55, no. 18, pp. 5569–5584, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. L. Lu and J. Zhao, “An automatic method for colon segmentation in virtual colonoscopy,” in Proceedings of the 4th International Conference on Biomedical Engineering and Informatics (BMEI '11), vol. 1, pp. 105–108, IEEE, Shanghai, China, October 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. T. A. Chowdhury and P. F. Whelan, “A fast and accurate method for automatic segmentation of colons at CT colonography based on colon geometrical features,” in Proceedings of the 15th Irish Machine Vision and Image Processing Conference (IMVIP '11), pp. 94–100, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. V. Taimouri, X. Liu, Z. Lai, C. Liu, D. Pai, and J. Hua, “Colon segmentation for prepless virtual colonoscopy,” IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 5, pp. 709–715, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. L. Lu and J. Zhao, “An improved method of automatic colon segmentation for virtual colon unfolding,” Computer Methods and Programs in Biomedicine, vol. 109, no. 1, pp. 1–12, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. N. Kilic, O. N. Ucan, and O. Osman, “Colon segmentation and colonic polyp detection using cellular neural networks and three-dimensional template matching,” Expert Systems, vol. 26, no. 5, pp. 378–390, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. http://cancerimagingarchive.net.
  14. P. A. Yushkevich, J. Piven, H. Cody, S. Ho, J. C. Gee, and G. Gerig, “User-guided level set segmentation of anatomical structures with ITK-SNAP,” Insight Journal, 2005, Special Issue on ISC/NA-MIC/MICCAI Workshop on Open-Source Software. View at Google Scholar
  15. N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. View at Publisher · View at Google Scholar · View at Scopus
  16. T. N. Pappas, “An adaptive clustering algorithm for image segmentation,” IEEE Transactions on Signal Processing, vol. 40, no. 4, pp. 901–914, 1992. View at Publisher · View at Google Scholar · View at Scopus
  17. Y. Yang and S. Huang, “Image segmentation by fuzzy C-means clustering algorithm with a novel penalty term,” Computing and Informatics, vol. 26, no. 1, pp. 17–31, 2007. View at Google Scholar · View at Scopus
  18. P. R. Reddy, V. Amarnadh, and M. Bhaskar, “Evaluation of stopping criterion in contour tracing algorithms,” International Journal of Computer Science and Information Technologies, vol. 3, no. 3, pp. 3888–3894, 2012. View at Google Scholar
  19. R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital Image Processing Using MATLAB, Pearson Prentice Hall, Upper Saddle River, NJ, USA, 2004.
  20. X. Li, Z. Liang, P. Zhang, and G. J. Kutcher, “An accurate colon residue detection algorithm with partial volume segmentation,” in Medical Imaging: Image Processing, vol. 5370 of Proceedings of SPIE, pp. 1419–1426, San Diego, Calif, USA, February 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. W. R. Crum, O. Camara, and D. L. G. Hill, “Generalized overlap measures for evaluation and validation in medical image analysis,” IEEE Transactions on Medical Imaging, vol. 25, no. 11, pp. 1451–1461, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. K. H. Zou, S. K. Warfield, A. Bharatha et al., “Statistical validation of image segmentation quality based on a spatial overlap index,” Academic Radiology, vol. 11, no. 2, pp. 178–189, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. A. M. Ali and A. A. Farag, “Graph cut based segmentation of multimodal images,” in IEEE International Symposium on Signal Processing and Information Technology (ISSPI '07), pp. 1036–1041, December 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. D. Chen, H. Abdelmunim, A. A. Farag, R. Falk, and G. Dryden, “Segmentation of colon tissue in CT colonography using adaptive level sets method,” in Proceedings of the MICCAI Workshop: Computational and Visualization Challenges in the New Era of Virtual Colonoscopy, 2008, pp. 108–115.