A Novel Image Fusion Method Using Beamlet Transform and Graph Cuts

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Graph cuts as an increasingly important tool for solving a number of energy minimization problems in computer vision and other fields, meanwhile beamlet transform as time-frequency and multiresolution analysis tool is often used in the domain of image processing, especially for image fusion. By analyzing the characters of DSA medical image, this paper proposes a novel DSA image fusion method which is combining beamlet transform and graph cuts theory. Firstly, the image was decomposed by beamlet transform to obtain the different subbands coefficients. Then an energy function based on graph cuts theory was constructed to adjust the weight of these coefficients to obtain an optimum fusion object. At last, an inverse of the beamlet transform reconstruct a synthesized DSA image which could contain more integrated accurate detail information of blood vessels. By contrast, the efficiency of our method is better than other traditional fusion methods.

Info:

Periodical:

Key Engineering Materials (Volumes 467-469)

Edited by:

Dehuai Zeng

Pages:

1092-1096

DOI:

10.4028/www.scientific.net/KEM.467-469.1092

Citation:

G. M. Zhang and Z. M. Cui, "A Novel Image Fusion Method Using Beamlet Transform and Graph Cuts", Key Engineering Materials, Vols. 467-469, pp. 1092-1096, 2011

Online since:

February 2011

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Price:

$35.00

[1] C. M. Bishop,: Pattern Recognition and Machine Learning. Springer. New York (2006).

[2] Y. Boykov, O. Veksler, and R. Zabih : Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence. 23(11) (2001), pp.1222-1239.

DOI: 10.1109/34.969114

[3] V. Kolmogorov, C. Rother : Minimizing nonsubmodular functions with graph cuts-A review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29(7) (2007), pp.1274-1279.

DOI: 10.1109/tpami.2007.1031

[4] V. Kolmogorov, R. Zabih : What energy functions can be minimized via graph cuts?. IEEE Transactions on Pattern Analysis and Machine Intelligence. 26(2) (2004) , pp.147-159.

DOI: 10.1109/tpami.2004.1262177

[5] L. Ying and E. Salari, : Beamlet Transform Based Technique for Pavement Image Processing and Classification, in Proceedings of 2009 IEEE International Conference on Electro/Information Technology, EIT 2009, pp.141-145.

DOI: 10.1109/eit.2009.5189598

[6] D. L. Donoho, Xiaoming Huo: Beamlets and Multiscale Image Analysis, http: /www-stat. stanford. edu/~donoho/Reports/2001/BeamletMSIP051101. pdf, (2001).

[7] D. L. Donoho, O. Levi,: Fast X-Ray and Beamlet Transforms for Three-D Data, http: /www-stat. stanford. edu/~donoho/Reports/2002/. Three-D-Beamlets. pdf, (2002).

[8] A. Goldberg, R. Tarjan,: A New Approach to the Maximum Flow Problem, J. ACM, vol. 35(1988), no. 4, pp.921-940.

[9] Gonzolez, R. C., Woods, R. E, in: Digital Image Processing second edition. Prentice Hall (2002).

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