Wavelet Based Image Fusion for Medical Applications

(*) Corresponding author

Authors' affiliations

DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)


Image fusion is the process by which complementary information from multiple images are integrated such that the resultant image contains more information than the individual source images. Medical image fusion is used to derive useful information from multimodality medical images which provides more information to the doctor for easy diagnosis of diseases. This paper presents the wavelet based image fusion algorithm on different multimodality medical images. Medical images to be fused are decomposed using Stationary Wavelet Transform (SWT). The low frequency coefficients are performed with a maximum-selection (MS) rule and high frequency coefficients are convolved with canny operator followed by the MS rule. The reconstructed image is obtained by performing the inverse SWT. The evaluation parameters Mean, Standard deviation, Average Gradient, Spatial frequency, Mutual Information, Edge based similarity measures are considered for evaluating the fused images. The performance of our method is compared with pixel averaging, PCA, DWT fusion methods. The experimental results show that the proposed framework provides better result for analysis of multimodality medical images.
Copyright © 2013 Praise Worthy Prize - All rights reserved.


Image Fusion; Stationary Wavelet Transform (SWT); Wavelet Coefficients

Full Text:



Yong Yang, Dong Sun Park, Shuying Huang and Nini Rao,” MedicImage Fusion via an Effective Wavelet-Based Approach”, EURASIP journal on Advances in Signal Processing, vol. 2010, Article ID 579341,13 pages,2010.

S. T. Shivappa, B. D. Rao, and M. M. Trivedi, “An iterative decoding algorithm for fusion of multimodal information,” EURASIP Journal on Advances in Signal Processing, vol. 2008, Article ID 478396, 10 pages, 2008.

Y. Wang and B. Lohmann, “Multisensor image fusion: concept, method and applications,” Tech. rep., Univ. Bremen.,2000.

S. Li and B. Yang, “Multifocus image fusion using region segmentation and spatial frequency,” Image and Vision Computing, vol. 26, no. 7, pp. 971–979, 2008.

P. J. Burt and E. H. Adelson,“The Laplacian pyramid as a compact image code,” IEEE Transactions on Communications, vol. 31, no. 4, pp. 532–540, 1983.

V.S.Petrovic and C. S. Xydeas, “Gradient-based multiresolution image fusion,” IEEE Transactions on Image Processing, vol.13, no. 2, pp. 228–237, 2004.

A.Toet, “Image fusion by a ratio of low-pass pyramid,” Pattern Recognition Letters, vol. 9, no. 4, pp. 245–253, 1989.

A. Toet, “A morphological pyramidal image decomposition ,” Pattern Recognition Letters, vol. 9, no.4, pp.255–261, 1989.

Hui Li, B.S Manjunath and Sanjit.K.Mitra,”Multi sensor image fusion using wavelet transform,” IEEE 0-8186-6950-1994.

Yufeng Zeheng, Edward A Essock,Bruce Hansen, Andrew M Haun, “A new metric based on spatial frequency and its application to dwt based fusion algorithm,” Science Direct: Information fusion ,vol.8 pp.177- 192, 2007.

T. Pu and G. Ni, “Contrast-based image fusion using the discrete wavelet transform,” Optical Engineering, vol. 39, no. 8, pp. 2075– 2082, 2000.

Wei-we1 wang, Peng-lang shui, Guo-xiang song, “Multifocus image fuson in wavelet domain,” Proceedings of the Second International Conference on Machine Learning and Cybernetics, 2-5 November, 2003.

J. Fowler, “The redundant discrete wavelet transform and additive noise,” IEEE Signal Processing Letters, vol.12(9), pp. 629–632, 2005.

F. Mai, Y. Hung, H. Zhong, and W. Sze.,”A hierarchical approach for fast and robust ellipse extraction.,” Pattern Recognition, vol :41(8), pp.2512–2524, August 2008.

S.Jayaraman, S.Esakkirajan, T.Veerakumar, ”Digital Image Processing ”,TMH, New Delhi,2009.

[Online]. Available: http://www.metapix.de/fusetool.zip

Qu Guihong, Zhang Dali, Yan Pingfan, “Information measure for performance of image fusion,” Electronics Letters, vol.38(7),pp. 313- 15,2000.

C. S. Xydeas and V. Petrovic, “Objective image fusion performance measure,” Electronics Letters , vol.36, no.4, pp.308- 309, 2000.

Luo, X., Wu, X., Multispectral and panchromatic image fusion using multi-feature and wavelet transform, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 2034-2040.


  • There are currently no refbacks.

Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2023 Praise Worthy Prize