Table of Contents Author Guidelines Submit a Manuscript
Applied Bionics and Biomechanics
Volume 2016, Article ID 3678913, 8 pages
http://dx.doi.org/10.1155/2016/3678913
Research Article

Singular Value Decomposition Based Features for Automatic Tumor Detection in Wireless Capsule Endoscopy Images

Electrical and Computer Engineering Department, University of Tabriz, Tabriz 51666 16471, Iran

Received 11 January 2016; Accepted 11 April 2016

Academic Editor: Alberto Borboni

Copyright © 2016 Vahid Faghih Dinevari 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. D. G. Adler and C. J. Gostout, “Wireless capsule endoscopy,” Hospital Physician, vol. 39, pp. 14–22, 2003. View at Google Scholar
  2. R. Sidhu, D. S. Sanders, K. Kapur, L. Marshall, D. P. Hurlstone, and M. E. McAlindon, “Capsule endoscopy: is there a role for nurses as physician extenders?” Gastroenterology Nursing, vol. 30, no. 1, pp. 45–50, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. J. T. Carlo, D. DeMarco, B. A. Smith et al., “The utility of capsule endoscopy and its role for diagnosing pathology in the gastrointestinal tract,” American Journal of Surgery, vol. 190, no. 6, pp. 886–890, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. M. T. Coimbra and J. P. S. Cunha, “MPEG-7 visual descriptors-contributions for automated feature extraction in capsule endoscopy,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 5, pp. 628–636, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. S. L. Parker, T. Tong, S. Bolden, and P. A. Wingo, “Cancer statistics, 1997,” CA: A Cancer Journal for Clinicians, vol. 47, no. 1, pp. 5–27, 1997. View at Publisher · View at Google Scholar · View at Scopus
  6. S. A. Karkanis, D. K. Iakovidis, D. E. Maroulis, D. A. Karras, and M. Tzivras, “Computer-aided tumor detection in endoscopic video using color wavelet features,” IEEE Transactions on Information Technology in Biomedicine, vol. 7, no. 3, pp. 141–152, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Li and M. Q.-H. Meng, “Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection,” IEEE Transactions on Information Technology in Biomedicine, vol. 16, no. 3, pp. 323–329, 2012. View at Publisher · View at Google Scholar · View at Scopus
  8. B. Li, M. Q.-H. Meng, and J. Y. W. Lau, “Computer-aided small bowel tumor detection for capsule endoscopy,” Artificial Intelligence in Medicine, vol. 52, no. 1, pp. 11–16, 2011. View at Publisher · View at Google Scholar
  9. M. M. Martins, D. J. Barbosa, J. Ramos, and C. S. Lima, “Small bowel tumors detection in capsule endoscopy by Gaussian modeling of color curvelet covariance coefficients,” in Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '10), pp. 5557–5560, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. I. Flamme, T. Frölich, and W. Risau, “Molecular mechanisms of vasculogenesis and embryonic angiogenesis,” Journal of Cellular Physiology, vol. 173, no. 2, pp. 206–210, 1997. View at Publisher · View at Google Scholar · View at Scopus
  11. R. A. Mathias, S. K. Gopal, and R. J. Simpson, “Contribution of cells undergoing epithelial-mesenchymal transition to the tumour microenvironment,” Journal of Proteomics, vol. 78, pp. 545–557, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. T. C. Wang and N. B. Karayiannis, “Detection of microcalcifications in digital mammograms using wavelets,” IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 498–509, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. S. G. Mallat, “Theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989. View at Publisher · View at Google Scholar · View at Scopus
  14. W.-L. Lee, Y.-C. Chen, and K.-S. Hsieh, “Ultrasonic liver tissues classification by fractal feature vector based on M-band wavelet transform,” IEEE Transactions on Medical Imaging, vol. 22, no. 3, pp. 382–392, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. G. H. Golub and C. F. Van Loan, Matrix Computations, JHU Press, 2012.
  16. B. Nagarajan and V. Devendran, “Singular value decomposition based features for vehicle classification under cluttered background and mild occlusion,” Procedia Engineering, vol. 30, pp. 529–534, 2012. View at Google Scholar
  17. K. W. Abyoto, S. J. Wirdjosoedirdjo, and T. Watanabe, “Unsupervised texture segmentation using multi-resolution analysis for feature extraction,” Journal of Tokyo University of Information Sciences, vol. 2, pp. 49–61, 1998. View at Google Scholar
  18. C. H. Li and P. C. Yuen, “Regularized color clustering in medical image database,” IEEE Transactions on Medical Imaging, vol. 19, no. 11, pp. 1150–1155, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Gevers, J. Van de Weijer, and H. Stokman, Color Image Processing: Methods and Applications: Color Feature Detection, CRC Press, New York, NY, USA, 2006.
  20. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer Science & Business Media, New York, NY, USA, 1995. View at Publisher · View at Google Scholar
  21. L. Wang, Support Vector Machines: Theory and Applications, Springer, Berlin, Germany, 2005.
  22. C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Mining and Knowledge Discovery, vol. 2, no. 2, pp. 121–167, 1998. View at Publisher · View at Google Scholar · View at Scopus
  23. L. Semler, L. Dettori, and J. Furst, “Wavelet-based texture classification of tissues in computed tomography,” in Proceedings 18th IEEE Symposium on Computer-Based Medical Systems, pp. 265–270, Dublin, Ireland, 2005.