Table of Contents
Erratum

An erratum for this article has been published. To view the erratum, please click here.

Advances in Artificial Neural Systems
Volume 2012 (2012), Article ID 457590, 7 pages
http://dx.doi.org/10.1155/2012/457590
Research Article

Unsupervised Neural Techniques Applied to MR Brain Image Segmentation

1Department of Communication Engineering, University of Malaga, 29071 Malaga, Spain
2Department of Signal Theory, Networking and Communications, University of Granada, 18071 Granada, Spain

Received 17 February 2012; Accepted 14 April 2012

Academic Editor: Anke Meyer-Baese

Copyright © 2012 A. Ortiz 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. I. A. Illán, J. M. Górriz, J. Ramírez et al., “18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis,” Information Sciences, vol. 181, no. 4, pp. 903–916, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. I. A. Illán, J. M. Górriz, M. M. López et al., “Computer aided diagnosis of Alzheimer's disease using component based SVM,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2376–2382, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. J. M. Górriz, F. Segovia, J. Ramírez, A. Lassl, and D. Salas-Gonzalez, “GMM based SPECT image classification for the diagnosis of Alzheimer's disease,” Applied Soft Computing Journal, vol. 11, no. 2, pp. 2313–2325, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. M. Kamber, R. Shinghal, D. L. Collins, G. S. Francis, and A. C. Evans, “Model-based 3-D segmentation of multiple sclerosis lesions in magnetic resonance brain images,” IEEE Transactions on Medical Imaging, vol. 14, no. 3, pp. 442–453, 1995. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Ramírez, J. M. Górriz, D. Salas-Gonzalez et al., “Computer-aided diagnosis of Alzheimer's type dementia combining support vector machines and discriminant set of features,” Information Sciences. In press.
  6. D. N. Kennedy, P. A. Filipek, and V. S. Caviness, “Anatomic segmentation and volumetric calculations in nuclear magnetic resonance imaging,” IEEE Transactions on Medical Imaging, vol. 8, no. 1, pp. 1–7, 1989. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Khan, S. F. Tahir, A. Majid, and T. S. Choi, “Machine learning based adaptive watermark decoding in view of anticipated attack,” Pattern Recognition, vol. 41, no. 8, pp. 2594–2610, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. Z. Yang and J. Laaksonen, “Interactive retrieval in facial image database using self-organizing maps,” in Proceedings of the MVA, 2005.
  9. M. García-Sebastián, E. Fernández, M. Graña, and F. J. Torrealdea, “A parametric gradient descent MRI intensity inhomogeneity correction algorithm,” Pattern Recognition Letters, vol. 28, no. 13, pp. 1657–1666, 2007. View at Publisher · View at Google Scholar · View at Scopus
  10. E. Fernández, M. Graña, and J. R. Cabello, “Gradient based evolution strategy for parametric illumination correction,” Electronics Letters, vol. 40, no. 9, pp. 531–532, 2004. View at Publisher · View at Google Scholar · View at Scopus
  11. M. García-Sebastián, A. Isabel González, and M. Graña, “An adaptive field rule for non-parametric MRI intensity inhomogeneity estimation algorithm,” Neurocomputing, vol. 72, no. 16-18, pp. 3556–3569, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. T. Kapur, L. Grimson, W. M. Wells, and R. Kikinis, “Segmentation of brain tissue from magnetic resonance images,” Medical Image Analysis, vol. 1, no. 2, pp. 109–127, 1996. View at Google Scholar · View at Scopus
  13. Y. F. Tsai, I. J. Chiang, Y. C. Lee, C. C. Liao, and K. L. Wang, “Automatic MRI meningioma segmentation using estimation maximization,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 3074–3077, September 2005. View at Scopus
  14. J. Xie and H. T. Tsui, “Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED),” Pattern Recognition Letters, vol. 25, no. 10, pp. 1133–1141, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. Y. Zhang, M. Brady, and S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm,” IEEE Transactions on Medical Imaging, vol. 20, no. 1, pp. 45–57, 2001. View at Publisher · View at Google Scholar · View at Scopus
  16. N. A. Mohamed, M. N. Ahmed, and A. Farag, “Modified fuzzy c-mean in medical image segmentation,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), pp. 3429–3432, March 1999. View at Scopus
  17. W. M. Wells III, W. E. L. Crimson, R. Kikinis, and F. A. Jolesz, “Adaptive segmentation of mri data,” IEEE Transactions on Medical Imaging, vol. 15, no. 4, pp. 429–442, 1996. View at Google Scholar · View at Scopus
  18. D. Tian and L. Fan, “A brain MR images segmentation method based on SOM neural network,” in Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE '07), pp. 686–689, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. I. Güler, A. Demirhan, and R. Karakiş, “Interpretation of MR images using self-organizing maps and knowledge-based expert systems,” Digital Signal Processing, vol. 19, no. 4, pp. 668–677, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. P. K. Sahoo, S. Soltani, and A. K. C. Wong, “A survey of thresholding techniques,” Computer Vision, Graphics and Image Processing, vol. 41, no. 2, pp. 233–260, 1988. View at Google Scholar · View at Scopus
  21. W. Sun, “Segmentation method of MRI using fuzzy Gaussian basis neural network,” Neural Information Processing, vol. 8, no. 2, pp. 19–24, 2005. View at Google Scholar
  22. J. Alirezaie, M. E. Jernigan, and C. Nahmias, “Automatic segmentation of cerebral MR images using artificial neural networks,” IEEE Transactions on Nuclear Science, vol. 45, no. 4, pp. 2174–2182, 1998. View at Google Scholar · View at Scopus
  23. A. Ortiz, J. M. Górriz, J. Ramírez, and D. Salas-Gonzalez, “MR brain image segmentation by hierarchical growing SOM and probability clustering,” Electronics Letters, vol. 47, no. 10, pp. 585–586, 2011. View at Google Scholar
  24. T. Kohonen, Self-Organizing Maps, Springer, 2001.
  25. E. Arsuaga and F. Díaz, “Topology preservation in SOM,” International Journal of Mathematical and Computer Sciences, vol. 1, no. 1, pp. 19–22, 2005. View at Google Scholar
  26. K. Taşdemir and E. Merényi, “Exploiting data topology in visualization and clustering of self-organizing maps,” IEEE Transactions on Neural Networks, vol. 20, no. 4, pp. 549–562, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. E. Alhoniemi, J. Himberg, J. Parhankagas, and J. Vesanta, “SOM Toolbox for Matlab v2.0,” 2005, http://www.cis.hut.fi/projects/somtoolbox.
  28. M. O. Stitson, J. A. E. Weston, A. Gammerman, V. Vork, and V. Vapnik, “Theory of support vector machines,” Tech. Rep. CSD-TR-96-17, Department of Computer Science, Royal Holloway College, University of London, 1996. View at Google Scholar
  29. M. Nixson and A. Aguado, Feature Extraction and Image Processing, Academic Press, 2008.
  30. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973. View at Google Scholar · View at Scopus
  31. M. Hu, “Visual pattern recognition by moments invariants,” IRE Transactions on Information Theory, vol. 8, pp. 179–187, 1962. View at Google Scholar
  32. Internet Brain Database Repository, Massachusetts General Hospital, Center for Morphometric Analysis, 2010, http://www.cma.mgh.harvard.edu/ibsr/data.html.
  33. J. C. Rajapakse and F. Kruggel, “Segmentation of MR images with intensity inhomogeneities,” Image and Vision Computing, vol. 16, no. 3, pp. 165–180, 1998. View at Google Scholar · View at Scopus
  34. J. L. Marroquin, B. C. Vemuri, S. Botello, F. Calderon, and A. Fernandez-Bouzas, “An accurate and efficient Bayesian method for automatic segmentation of brain MRI,” IEEE Transactions on Medical Imaging, vol. 21, no. 8, pp. 934–945, 2002. View at Publisher · View at Google Scholar · View at Scopus
  35. J. C. Bezdek, L. O. Hall, and L. P. Clarke, “Review of MR image segmentation techniques using pattern recognition,” Medical Physics, vol. 20, no. 4, pp. 1033–1048, 1993. View at Publisher · View at Google Scholar · View at Scopus
  36. L. P. Clarke, R. P. Velthuizen, M. A. Camacho et al., “MRI segmentation: methods and applications,” Magnetic Resonance Imaging, vol. 13, no. 3, pp. 343–368, 1995. View at Publisher · View at Google Scholar · View at Scopus
  37. C. T. Su and H. C. Lin, “Applying electromagnetism-like mechanism for feature selection,” Information Sciences, vol. 181, no. 5, pp. 972–986, 2011. View at Publisher · View at Google Scholar · View at Scopus
  38. K. Tan, E. Khor, and T. Lee, Multiobjective Evolutionary and Applications, Springer, 1st edition, 2005.
  39. T. Tasdizen, S. P. Awate, R. T. Whitaker, and N. L. Foster, “MRI tissue classification with neighborhood statistics: a nonparametric, entropy-minimizing approach,” in Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI '05), 2005.
  40. I. Usman and A. Khan, “BCH coding and intelligent watermark embedding: employing both frequency and strength selection,” Applied Soft Computing Journal, vol. 10, no. 1, pp. 332–343, 2010. View at Publisher · View at Google Scholar · View at Scopus
  41. Y. Wang, T. Adali, S. Y. Kung, and Z. Szabo, “Quantification and segmentation of brain tissues from MR images: a probabilistic neural network approach,” IEEE Transactions on Image Processing, vol. 7, no. 8, pp. 1165–1181, 1998. View at Google Scholar · View at Scopus