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Comparative Study of Different Wavelet Transforms in Fusion of Multimodal Medical Images

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
R. E. Masumdar, R. G. Karandikar
10.5120/ijca2016910899

R E Masumdar and R G Karandikar. Comparative Study of Different Wavelet Transforms in Fusion of Multimodal Medical Images. International Journal of Computer Applications 146(11):18-24, July 2016. BibTeX

@article{10.5120/ijca2016910899,
	author = {R. E. Masumdar and R. G. Karandikar},
	title = {Comparative Study of Different Wavelet Transforms in Fusion of Multimodal Medical Images},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2016},
	volume = {146},
	number = {11},
	month = {Jul},
	year = {2016},
	issn = {0975-8887},
	pages = {18-24},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume146/number11/25442-2016910899},
	doi = {10.5120/ijca2016910899},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

This paper presents a comparative study of image fusion of MRI and CT images using various wavelet transforms. The fusion of the images is done by implementing a multi-resolution decomposition method with the help of various wavelets. Entropy, PSNR and MSE are the parameters that are used as performance metrics of the fusion done using various wavelets. The MRI and CT images are then fused using the select maximum fusion rule, since studies have shown that select maximum rule provides the best result. The final fused image is examined using the various performance metrics to evaluate which wavelet gives the best result.

References

  1. Bushara N. Kayani, Anwar Majeed Mirza, Ajmal Bangash, Harron Iftikhar, "Pixel and Feature Level Multiresolution Image Fusion Based On Fuzzy Logic", in Innovations and Advanced Techniques in Computer and Information Sciences and Engineering, 2007, pp 129-132.
  2. Shweta K. Shah, Prof. D. U. Shah. (2013, Mar.). Comparative Study of Image Fusion Techniques based on Spatial and Tranform Domain. International Journal of Innovative Research in Science, Engineering and Technology. [Online]. 3(3).
  3. Rohan Ashok Mandhare, Pragati Upadhyay, Sudha Gupta. (2013, June). Pixel Level Image Fusion Using Brovey Transform and Wavelet transform. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering. [Online]. 2(6). http://www.ijareeie.com/upload/june/25F_PIXEL.pdf.
  4. V. P. S. Naidu, J. R. Raol. (2008, May.). Pixel Level Image Fusion Using Wavelets and Principal Component Analysis
  5. Myungjin Choi, (2006, June) ,"A New Intensity-Hue-Saturation Fusion Approach to Image Fusion With a Tradeoff Parameter", IEEE Transactions on Geoscience and Remote Sensing, 44(6).
  6. H. Li, B. S. Manjunath, S. K. Mitra. "Multisensor image fusion using the wavelet transform", Graphical Models and Image Processing, 57(3), pp. 235– 245, 1995.
  7. Tianjiao Zeng, Renyi Hu, Yaodong He, and Yunqi Wang, “Image Fusion Using Laplacian Pyramid Transform,” ECE Capstone Design Project, Rutgers School of Engineering, Spring 2014.
  8. A. Grossmann and J. Morlet, “Decomposition of Hardy functions into square integrable wavelets of constant shape,” SIAM J. Math.. vol. 15. pp. 723-736. 1984
  9. Y. Meyer. “Ondelettes et foncttons splines,” Srm. Equarions auxeri1,ee.s Par-tie//es. Ecole Polytechnique. Paris, France, Dec. 1986.
  10. S. Mallat, A Wavelet Tour of Signal Processing, Academic Press,1999.
  11. Gonzalo Pajares, Jesus Manuel de la Cruz."A wavelet-based image fusion tutorial", Pattern recognition. 37, Elsevier, pp, 1855-1872, 2004
  12. Haar A., "Zur Theorie der orthogonalen Funktionensysteme. Mathematische Annalen", 69, 331–371.
  13. Porwik P., Lisowska A," The New Graphic Description of the Haar Wavelet Transform". Lecture Notes in Computer Science, Springer–Verlag, Berlin, Heidelberg, New York, 3039, 1–8.
  14. Zeng L., Jansen C. P., Marsch S., Unser M., Hunziker R,." Four– Dimensional Wavelet Compression of Arbitrarily Sized Echocardiographic Data", IEEE Transactions on Medical Imaging, 21(9), 1179–1188. 2003.
  15. Claypoole R., Davis G., Sweldens W., Baraniuk R. "Adaptive Wavelet Transforms for Image Coding" Asilomar Conference on Signals, Systems and Computers.
  16. Munoz A., Ertle R., Unser M."Continuous wavelet transform with arbitrary scales and O(N) complexity", Signal Processing, 82, 749–757.
  17. Z. Zhang, R.S. Blum, "A categorization of multi-scale decomposition based image fusion schemes with a performance study for a digital camera application", Proc. IEEE 87 (8) (1999) 1315–1326.
  18. J.-B. Martens and L. Meesters, “Image dissimilarity,” Signal Processing, Vol. 70, pp. 155-176, Nov. 1998.
  19. VQEG, “Final report from the video quality experts group on the validation of objective models of video quality assessment,” http://www.vqeg.org/, Mar. 2000.
  20. A. M. Eskicioglu and P.S. Fisher, “Image quality measures and their performance,” IEEE Trans. Communications, vol. 34, pp. 2959-2965, Dec. 1995.

Keywords

Computed Tomography, Magnetic Resonance Imaging, Wavelet Transform, Haar Transform, Daubechies Transform, Symlet Transform, Image Fusion Vanishing Moments, Multiresolution Decomposition, Image Fusion, Quadrature Mirror Filter, Order, Filter Banks, Mean Squarred Error, Peak Signal to Noise Ratio, Entropy