Table of Contents Author Guidelines Submit a Manuscript
Advances in Artificial Intelligence
Volume 2010 (2010), Article ID 520427, 15 pages
http://dx.doi.org/10.1155/2010/520427
Research Article

3D Medical Volume Segmentation Using Hybrid Multiresolution Statistical Approaches

1Department of Electronic & Computer Engineering, School of Engineering and Design, Brunel University, West London, Uxbridge UB8 3PH, UK
2Nanotechnology and Integrated BioEngineering Centre (NIBEC), Faculty of Computing and Engineering, University of Ulster, Shore Road Newtownabbey Co. Antrim BT37 0QB, Northern Ireland

Received 8 February 2010; Revised 24 May 2010; Accepted 24 June 2010

Academic Editor: Chu-Song Chen

Copyright © 2010 Shadi AlZu'bi and Abbes Amira. 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. P. Schelkens, A. Munteanu, J. Barbarien, M. Galca, X. Giro-Nieto, and J. Cornelis, “Wavelet coding of volumetric medical datasets,” IEEE Transactions on Medical Imaging, vol. 22, no. 3, pp. 441–458, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. D. Montgomery, Multiscale compression and segmentation of volumetric oncological PET imagery, Ph.D. thesis, 2006.
  3. D. W. G. Montgomery, A. Amira, and H. Zaidi, “Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model,” Medical Physics, vol. 34, no. 2, pp. 722–736, 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. H. Zaidi, M. Diaz-Gomez, A. Boudraa, and D. O. Slosman, “Fuzzy clustering-based segmented attenuation correction in whole-body PET imaging,” Physics in Medicine and Biology, vol. 47, no. 7, pp. 1143–1160, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. R. Gonzalez and R. Woods, Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, USA, 2nd edition, 2001.
  6. A. Jensen and A. la Cour-Harbo, Ripples in Mathematics: The Discrete Wavelet Transform, Springer, Berlin, Germany, 2001.
  7. D. Agrafiotis, D. Bull, and N. Canagarajah, “Three-dimensional coding of volumetric medical images using regions of interest,” in Proceedings of the International Conference on Visual Information Engineering (VIE '03), pp. 194–197, July 2003.
  8. J. Li, A. Najmi, and R. M. Gray, “Image classification by a two-dimensional hidden Markov model,” IEEE Transactions on Signal Processing, vol. 48, no. 2, pp. 517–533, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Li and J. Z. Wang, “Studying digital imagery of ancient paintings by mixtures of stochastic models,” IEEE Transactions on Image Processing, vol. 13, no. 3, pp. 340–353, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. 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
  11. A. Amira, S. Chandrasekaran, D. W. G. Montgomery, and I. Servan Uzun, “A segmentation concept for positron emission tomography imaging using multiresolution analysis,” Neurocomputing, vol. 71, no. 10–12, pp. 1954–1965, 2008. View at Publisher · View at Google Scholar · View at Scopus
  12. S. G. Chang, B. Yu, and M. Vetterli, “Spatially adaptive wavelet thresholding with context modeling for image denoising,” IEEE Transactions on Image Processing, vol. 9, no. 9, pp. 1522–1531, 2000. View at Google Scholar · View at Scopus
  13. X. Zhang and M. Desai, “Wavelet based automatic thresholding for image segmentation,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '97), vol. 1, pp. 224–227, 1997.
  14. I. Daubechies, “Wavelet transforms and orthonormal wavelet bases,” in Different Perspectives on Wavelets (San Antonio, Tex, 1993), vol. 47 of Proceedings of Symposia in Applied Mathematics, pp. 1–33, American Mathematical Society, Providence, RI, USA, 1993. View at Google Scholar
  15. S. G. Mallat, “A 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
  16. K. Rajpoot and N. Rajpoot, “Hyperspectral colon tissue cell classification,” in Medical Imaging, Proceedings of SPIE, 2004.
  17. I. S. Uzun and A. Amira, “Design and FPGA implementation of finite ridgelet transform,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '05), vol. 6, pp. 5826–5829, May 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. E. J. Stollnitz, T. D. DeRose, and D. H. Salestin, “Wavelets for computer graphics: a primer—part 1,” IEEE Computer Graphics and Applications, vol. 15, no. 3, pp. 76–84, 1995. View at Publisher · View at Google Scholar · View at Scopus
  19. A. Haar, “Zur theorie der orthogonalen funktionensysteme—erste mitteilung,” Mathematische Annalen, vol. 69, no. 3, pp. 331–371, 1910. View at Publisher · View at Google Scholar · View at Scopus
  20. Z. Shi and R. Shibasaki, “An approach to image segmentation using multiresolution analysis of wavelets,” in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, vol. 6, pp. 810–815, IEEE, Tokyo, Japan, 1999.
  21. M. N. Do and M. Vetterli, “Orthonormal finite ridgelet transform for image compression,” in Proceedings of the International Conference on Image Processing (ICIP '00), vol. 2, pp. 367–370, September 2000. View at Scopus
  22. E. Cand'es and D. Donoho, “Curvelets—a surprisingly effective nonadaptive representation for objects with edges,” in Curves and Surfaces, pp. 105–120, Vanderbilt University Press, 2000. View at Google Scholar
  23. E. Candes, Ridgelets: theory and application, Ph.D. thesis, Department of Statistics, Stanford University, Stanford, Calif, USA, 1998.
  24. E. J. Candès and D. L. Donoho, “Ridgelets: a key to higher-dimensional intermittency?” Philosophical Transactions of the Royal Society A, vol. 357, no. 1760, pp. 2495–2509, 1999. View at Google Scholar · View at Scopus
  25. J.-L. Starck, E. J. Candès, and D. L. Donoho, “The curvelet transform for image denoising,” IEEE Transactions on Image Processing, vol. 11, no. 6, pp. 670–684, 2002. View at Publisher · View at Google Scholar · View at Scopus
  26. J. He, “A characterization of inverse Radon transform on the Laguerre hypergroup,” Journal of Mathematical Analysis and Applications, vol. 318, no. 1, pp. 387–395, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. A. Balter, Dicom to 3 Dimentional, Pacific Northwest National Laboratory, 2009.
  28. H. Biao, L. Fang, and J. Licheng, “Linear feature detection based on ridgelet,” Science in China, Series E, vol. 46, no. 2, 2003. View at Google Scholar · View at Scopus
  29. Computed Tomography Scanner, King Abdullah University Hospital, Ramtha, Jordan, 2009.
  30. J. Lu and L. Carin, “HMM-based multiresolution image segmentation,” in Proceedings of the IEEE International Conference on Acoustic, Speech, and Signal Processing, pp. 3357–3360, May 2002. View at Scopus
  31. A. Huang, R. Abugharbieh, and R. Tam, “Image segmentation using an efficient rotationally invariant 3D region-based hidden Markov model,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '08), pp. 107–111, Anchorage, Alaska, USA, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. D. DeMonthon and M. Vuilleumier, “Image distance using hidden Markov models,” in Proceedings of the 15th International Conference on Pattern Recognition (ICPR '00), vol. 3, p. 3147, 2000.
  33. J. Jiten and B. Merialdo, “Semantic image segmentation with a multidimensional hidden Markov model,” in Proceedings of the 13th International Multimedia Modeling Conference, vol. 4351 of Lecture Notes in Computer Science, pp. 616–624, Springer, Singapore, 2007. View at Publisher · View at Google Scholar
  34. Y. He and A. Kundu, “2-D shape classification using hidden Markov model,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 11, pp. 1172–1184, 1991. View at Publisher · View at Google Scholar · View at Scopus
  35. N. Arica and F. T. Yarman Vural, “A shape descriptor based on circular hidden Markov model,” in Proceedings of the 15th IEEE International Conference on Pattern Recognition, vol. 1, pp. 924–927, 2000. View at Scopus
  36. J. Solomon, J. A. Butman, and A. Sood, “Segmentation of brain tumors in 4D MR images using the hidden Markov model,” Computer Methods and Programs in Biomedicine, vol. 84, no. 2-3, pp. 76–85, 2006. View at Publisher · View at Google Scholar · View at Scopus
  37. L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257–286, 1989. View at Publisher · View at Google Scholar · View at Scopus
  38. X. Ma, D. Schonfeld, and A. Khokhar, “Distributed multi-dimensional hidden Markov model: theory and application in multiple-object trajectory classication and recognition,” in Multimedia Content Access: Algorithms and Systems II, Proceedings of SPIE, 2008.
  39. W. Jentzen, L. Freudenberg, E. G. Eising, M. Heinze, W. Brandau, and A. Bockisch, “Segmentation of PET volumes by iterative image thresholding,” Journal of Nuclear Medicine, vol. 48, no. 1, pp. 108–114, 2007. View at Google Scholar · View at Scopus
  40. 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
  41. International Electrotechnical Commission (IEC), 61675-1, Geneva, Switzerland, 1998, National Electrical Manufacturers Association (NEMA), Standards Publication No. NU2, Washington, DC, USA, 2001.
  42. K. H. Zou, S. K. Warfield, A. Bharatha et al., “Statistical validation of image segmentation quality based on a spatial overlap, index 1, scientific reports,” Academic Radiology, vol. 11, no. 2, pp. 178–189, 2004. View at Publisher · View at Google Scholar · View at Scopus