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Reseach Article

Medical Image Fusion based on Shearlets and Human Feature Visibility

by Nemir Ahmed Al-Azzawi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 125 - Number 12
Year of Publication: 2015
Authors: Nemir Ahmed Al-Azzawi
10.5120/ijca2015906147

Nemir Ahmed Al-Azzawi . Medical Image Fusion based on Shearlets and Human Feature Visibility. International Journal of Computer Applications. 125, 12 ( September 2015), 7-12. DOI=10.5120/ijca2015906147

@article{ 10.5120/ijca2015906147,
author = { Nemir Ahmed Al-Azzawi },
title = { Medical Image Fusion based on Shearlets and Human Feature Visibility },
journal = { International Journal of Computer Applications },
issue_date = { September 2015 },
volume = { 125 },
number = { 12 },
month = { September },
year = { 2015 },
issn = { 0975-8887 },
pages = { 7-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume125/number12/22482-2015906147/ },
doi = { 10.5120/ijca2015906147 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:15:50.135583+05:30
%A Nemir Ahmed Al-Azzawi
%T Medical Image Fusion based on Shearlets and Human Feature Visibility
%J International Journal of Computer Applications
%@ 0975-8887
%V 125
%N 12
%P 7-12
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Medical image fusion is a technique that integrates complementary information from multimodality images. The fused image is more suitable for treatment plan strategies. In this paper, an efficient medical image fusion method has been proposed based on shearlet transform and human visibility feature as fusion rule. Image fusion rule is the solution that influences the quality of image fusion. The multimodal medical images were first decomposed using the shearlet transform then fusion rules were applied to shearlet coefficients. The low-frequency coefficients are fused by human visibility feature method. While, the high frequency coefficients are fused by the maximum selection fusion rule. The final fusion image is obtained by directly applying inverse shearlet transform to the fused coefficients. The technique proposed has successfully been used in CT/MRI image fusion for tumor diagnosis. The visual experiments and quantitative assessments demonstrate the effectiveness of this method compared to present image fusion.

References
  1. N. Al-Azzawi, H. A. M. Sakim, W. A. K. W. Abdullah et al., "Medical image fusion scheme using complex contourlet transform based on PCA." pp. 5813-5816.
  2. S. F. Nemec, M. A. Donat, S. Mehrain et al., “CT-MR image data fusion for computer assisted navigated neurosurgery of temporal bone tumors,” European Journal of Radiology, vol. 62, no. 2, pp. 192-198, 2007.
  3. P. Patias, “Medical imaging challenges photogrammetry,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 56, no. 5-6, pp. 295-310, 2002.
  4. G. Pajares, and J. Manuel de la Cruz, “A wavelet-based image fusion tutorial,” Pattern Recognition, vol. 37, no. 9, pp. 1855-1872, 2004.
  5. S. Li, and B. Yang, “Hybrid multiresolution method for multisensor multimodal image fusion,” IEEE sensor Journal, vol. 10, no. 9, pp. 1519-1526, 2010.
  6. L. Yang, B. L. Guo, and W. Ni, “Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform,” Neurocomputing, vol. 72, no. 1–3, pp. 203-211, 2008.
  7. M. Aguilar, and J. R. New, "Fusion of multi-modality volumetric medical imagery." pp. 1206-1212.
  8. L. Yang, B. L. Guo, and W. Ni, “Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform,” Neurocomputing, vol. 72, no. 1-3, pp. 203-211, 2008.
  9. N. Al-Azzawi, H. A. M. Sakim, and W. A. K. W. Abdullah, "An efficient medical image fusion method using contourlet transform based on PCA," ISIEA 2009 - IEEE Symposium on Industrial Electronics and Applications, Proceedings. pp. 11-19, 2009.
  10. S. Li, H. Yin, and L. Fang, “Group-sparse representation with dictionary learning for medical image denoising and fusion,” Biomedical Engineering, IEEE Transactions on, vol. 59, no. 12, pp. 3450-3459, 2012.
  11. N. Al-Azzawi, and W. A. K. W. Abdullah, "Medical Image Fusion Schemes Using Contourlet Transform and PCA Bases," Image Fusion and Its Applications, Y. Zheng, ed., pp. 93-110, InTech: Janeza Trdine 9, 51000 Rijeka, Croatia, 2011.
  12. A. Toet, L. V. Ruyven, and J. Velaton, “Merging thermal and visual images by a contrast pyramid,” Optical Engineering, vol. 28, no. 7, pp. 789-792, 1989.
  13. K. Guo, G. Kutyniok, and D. Labate, “Sparse multidimensional representations using anisotropic dilation and shear operators,” Wavelets und Splines (Athens, GA, 2005), G. Chen und MJ Lai, eds., Nashboro Press, Nashville, TN, pp. 189-201, 2006.
  14. D. L. Donoho, “Sparse components of images and optimal atomic decompositions,” Constructive Approximation, vol. 17, no. 3, pp. 353-382, 2001.
  15. K. Guo, and D. Labate, “Optimally sparse multidimensional representation using shearlets,” SIAM journal on mathematical analysis, vol. 39, no. 1, pp. 298-318, 2007.
  16. K. Guo, D. Labate, and W.-Q. Lim, “Edge analysis and identification using the continuous shearlet transform,” Applied and Computational Harmonic Analysis, vol. 27, no. 1, pp. 24-46, 2009.
  17. S. Liu, M. Shi, Z. Zhu et al., “Image fusion based on complex-shearlet domain with guided filtering,” Multidimensional Systems and Signal Processing, pp. 1-18, 2015.
  18. F. Sillion, and G. Drettakis, "Feature-based control of visibility error: A multi-resolution clustering algorithm for global illumination." pp. 145-152.
  19. J. W. Huang, Y. Q. Shi, and X. H. Dai, “A segmentation-based image coding algorithm using the features of human vision system,” Journal Image Graphics, vol. 4, no. 5, pp. 400–404, 1999.
  20. N. Al-Azzawi, H. A. M. Sakim, and W. A. K. W. Abdullah, "Fast free-form registration based on kullback- leibler distance for multimodal medical image." pp. 484-489.
  21. W. Chan, and J. Sivaswamy, “Segmentation of Text Embedded in Clutter using Local Energy,” in Proceeding of the IASTED International Conference on Signal and Image Processing, 1998, pp. 364-368.
  22. S. Li, J. T. Kwok, and Y. Wang, “Multifocus image fusion using artificial neural networks,” Pattern Recognition Letters, vol. 23, no. 8, pp. 985-997, 2002.
  23. N. M. Oliver, B. Rosario, and A. P. Pentland, “A Bayesian computer vision system for modeling human interactions,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 831-843, 2000.
  24. M. C. Morrone, and D. Burr, “Feature detection in human vision: A phase-dependent energy model,” Proceedings of the Royal Society of London B: Biological Sciences, vol. 235, no. 1280, pp. 221-245, 1988.
  25. E. F. Erickson, and R. M. Brown, “Calculation of Fringe Visibility in a Laser-Illuminated Interferometer,” J. Opt. Soc. Amer., vol. 57, no. 3, 1967.
  26. P. Burt, “The pyramid as a structure for efficient computation,” Multiresolution Image Processing and Analysis, A. Rosenfeld, Ed. Springer-Verlag, Berlin, pp. 6-35, 1984.
  27. Z. Li, Z. Jing, X. Yang et al., “Color transfer based remote sensing image fusion using non-separable wavelet frame transform,” Pattern Recognition Letters, vol. 26, no. 13, pp. 2006-2014, 2005.
  28. Z. Wang, and A. C. Bovik, “A universal image quality index,” IEEE Signal Processing Letters, vol. 9, no. 3, pp. 81-84, 2002.
  29. G. Piella, and H. Heijmans, "A new quality metric for image fusion." pp. 173-176.
Index Terms

Computer Science
Information Sciences

Keywords

Shearlet transform medical image fusion human visibility feature multimodality CT/MRI image.