Table of Contents
Advances in Artificial Neural Systems
Volume 2013, Article ID 278241, 18 pages
Research Article

Novel Discrete Compactness-Based Training for Vector Quantization Networks: Enhancing Automatic Brain Tissue Classification

Computer Engineering Institute, The Technological University of the Mixteca (UTM), Carretera Huajuapan-Acatlima Km 2.5, 69004 Huajuapan de León, OAX, Mexico

Received 27 June 2013; Revised 19 September 2013; Accepted 18 November 2013

Academic Editor: Juan Ignacio Arribas

Copyright © 2013 Ricardo Pérez-Aguila. 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. Bribiesca, “Measuring 2-D shape compactness using the contact perimeter,” Computers and Mathematics with Applications, vol. 33, no. 11, pp. 1–9, 1997. View at Google Scholar · View at Scopus
  2. N. B. M. Yusof, Multilevel Learning in Kohonen SOM Network for Classification Problems, Universiti Teknologi Malaysia, 2006.
  3. R. Kamimura, S. Aida-Hyugaji, and Y. Maruyama, “Information-theoretic self-organizing maps with minkowski distance,” in Proceedings of the 17th IASTED International Conference on Artificial Intelligence and Soft Computing, pp. 15–20, ASC, July 2003. View at Scopus
  4. M. Porrmann, M. Franzmeier, H. Kalte, U. Witkowski, and U. A. Rückert, “Reconfigurable SOM hardware accelerator,” in Proceedings of the 10th European Symposium on Artificial Neural Networks (ESANN '02), pp. 337–342, Bruges, Belgium, April 2004.
  5. R. Pérez-Aguila, “Automatic segmentation and classification of computed tomography brain images: an approach using one-dimensional Kohonen networks,” IAENG International Journal of Computer Science, vol. 37, no. 1, pp. 27–35, 2010. View at Google Scholar
  6. R. Pérez-Aguila, “Brain tissue characterization via non-supervised one-dimensional Kohonen networks,” in Proceedings of the 19th International Conference on Electronics, Communications and Computers (CONIELECOMP '09), pp. 197–201, IEEE Computer Society, Puebla, México, February 2009.
  7. R. Pérez-Aguila, “Enhancing brain tissue segmentation and image classification via 1D Kohonen networks and discrete compactness: an experimental study,” Engineering Letters, vol. 21, no. 4, pp. 171–180, 2013. View at Google Scholar
  8. E. Davalo and P. Naïm, Neural Networks, The Macmillan Press, 1992.
  9. H. Ritter, T. Martinetz, and K. Schulten, Neural Computation and Self-Organizing Maps: An Introduction, Addison-Wesley, 1992.
  10. J. Hilera and V. Martínez, Redes Neuronales Artificiales, Alfaomega, México, 2000, (Spanish).
  11. D. Niebur, “An example of unsupervised networks Kohonen’s self-organizing feature map,” Technical Report, Jet Propulsion Laboratory & California Institute of Technology, 1995, View at Google Scholar
  12. R. Pérez-Aguila, P. Gómez-Gil, and A. Aguilera, “Non-supervised classification of 2D color images using Kohonen networks and a novel metric,” in Progress in Pattern Recognition, Image Analysis and Applications, vol. 3773 of Lecture Notes in Computer Science, pp. 271–284, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  13. M. Pöllä, T. Honkela, and T. Kohonen, “Bibliography of self-organizing map (SOM) papers: 2002–2005 addendum,” TKK Reports in Information and Computer Science TKK-ICS-R23, Department of Information and Computer Science, Faculty of Information and Natural Sciences, Helsinki University of Technology, 2009, View at Google Scholar
  14. E. Bribiesca and R. S. Montero, “State of the art of compactness and circularity measures,” International Mathematical Forum, vol. 4, no. 27, pp. 1305–1335, 2009. View at Google Scholar
  15. S. Marchand-Maillet and Y. M. Sharaiha, Binary Digital Image Processing: A Discrete Approach, Academic Press, 2000.
  16. R. Osserman, “The isoperimetric inequality,” Bulletin of the American Mathematical Society, vol. 84, no. 6, pp. 1182–1238, 1978. View at Google Scholar
  17. J. Einenkel, U.-D. Braumann, L.-C. Horn et al., “Evaluation of the invasion front pattern of squamous cell cervical carcinoma by measuring classical and discrete compactness,” Computerized Medical Imaging and Graphics, vol. 31, no. 6, pp. 428–435, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. A. B. Abche, A. Maalouf, and E. Karam, “A hybrid approach for the segmentation of MRI brain images,” in Proceedings of the IEEE 13th International Conference on Systems, Signals and Image Processing, September 2006.
  19. F. Peng, K. Yuan, S. Feng, and W. Chen, “Pre-processing of CT brain images for content-based image retrieval,” in Proceedings of the 1st International Conference on BioMedical Engineering and Informatics (BMEI '08), pp. 208–212, Sanya, China, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Berthod, Z. Kato, S. Yu, and J. Zerubia, “Bayesian image classification using Markov random fields,” Image and Vision Computing, vol. 14, no. 4, pp. 285–295, 1996. View at Publisher · View at Google Scholar · View at Scopus
  21. R. Pérez-Aguila, Orthogonal polytopes: study and application [Ph.D. thesis], Universidad de las Américas Puebla (UDLAP), 2006,
  22. R. Pérez-Aguila, “Representing and visualizing vectorized videos through the extreme vertices model in the n-dimensional space (nD-EVM),” Journal Research in Computer Science, vol. 29, pp. 65–80, 2007. View at Google Scholar
  23. BrainWeb: Simulated Brain Database, 2013,
  24. C. A. Cocosco, V. Kollokian, R. K.-S. Kwan, and A. C. Evans, “Brain web: online interface to a 3D MRI simulated brain database,” NeuroImage, vol. 5, no. 4, part 2, article S425, 1997. View at Google Scholar · View at Scopus
  25. D. L. Collins, A. P. Zijdenbos, V. Kollokian et al., “Design and construction of a realistic digital brain phantom,” IEEE Transactions on Medical Imaging, vol. 17, no. 3, pp. 463–468, 1998. View at Google Scholar · View at Scopus
  26. R. K.-S. Kwan, A. C. Evans, and G. B. Pike, “An extensible MRI simulator for post-processing evaluation,” in Visualization in Biomedical Computing, vol. 1131 of Lecture Notes in Computer Science, pp. 135–140, Springer, 1996. View at Publisher · View at Google Scholar
  27. R. K.-S. Kwan, A. C. Evans, and B. Pike, “MRI simulation-based evaluation of image-processing and classification methods,” IEEE Transactions on Medical Imaging, vol. 18, no. 11, pp. 1085–1097, 1999. View at Publisher · View at Google Scholar · View at Scopus
  28. F. S. McDonald, P. S. Mueller, and G. Ramakrishna, Mayo Clinic Images in Internal Medicine, Informa HealthCare, 1st edition, 2004.
  29. R. Pérez-Aguila, Una Introducción al Cómputo Neuronal Artificial, El Cid Editor, Argentina, 2012, (Spanish).