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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 835481, 12 pages
Research Article

Log-Gabor Energy Based Multimodal Medical Image Fusion in NSCT Domain

1School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China
2School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330032, China
3Institute of Biomedical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Received 7 June 2014; Revised 5 August 2014; Accepted 6 August 2014; Published 24 August 2014

Academic Editor: Ezequiel López-Rubio

Copyright © 2014 Yong Yang et al. 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.


Multimodal medical image fusion is a powerful tool in clinical applications such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning. In this paper, a novel nonsubsampled Contourlet transform (NSCT) based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena. The source medical images are initially transformed by NSCT followed by fusing low- and high-frequency components. The phase congruency that can provide a contrast and brightness-invariant representation is applied to fuse low-frequency coefficients, whereas the Log-Gabor energy that can efficiently determine the frequency coefficients from the clear and detail parts is employed to fuse the high-frequency coefficients. The proposed fusion method has been compared with the discrete wavelet transform (DWT), the fast discrete curvelet transform (FDCT), and the dual tree complex wavelet transform (DTCWT) based image fusion methods and other NSCT-based methods. Visually and quantitatively experimental results indicate that the proposed fusion method can obtain more effective and accurate fusion results of multimodal medical images than other algorithms. Further, the applicability of the proposed method has been testified by carrying out a clinical example on a woman affected with recurrent tumor images.