Now-a-days, almost all areas of medical diagnosis are impacted by the digital image processing. When an image is processed for visual interpretation, the human eye is the judge of how well a particular method works. Clinical application demanding Radiotherapy plan, for instance, often benefits from the complementary information in images of different modalities. For medical diagnosis, Computed Tomography (CT) provides the best information on denser tissue with less distortion. Magnetic Resonance Image (MRI) provides better information on soft tissue with more distortion. With more available multimodality medical images in clinical applications, the idea of combining images from different modalities become very important and medical image fusion has emerged as a new promising research field. Wavelet transform fusion is more formally defined by considering the wavelet transforms of the two registered input images together with the fusion rule . Then, the inverse wavelet transform is computed, and the fused image is reconstructed. The wavelets used in image fusion can be classified into three categories Orthogonal, Bi-orthogonal and A'trous'wavelet. Although these wavelets share some common properties, each wavelet has a unique image decompression and reconstruction characteristics that lead to different fusion results. A Novel multi-resolution fusion algorithm is proposed in this paper, which combines aspects of region and pixel based fusion. Normally, when a wavelet transformation alone is applied the results are not so useful for analysis. However if a wavelet transform and a traditional transform such as Principal Component Analysis(PCA) transform is integrated, better fusion results may be achieved. Hence a new novel approach is introduced in this work to improve the fusion method by integrating with PCA transforms. In this paper the fusion results are compared visually and statistically to show that wavelet integrated method can improve the fusion result, reduce the ringing or aliasing effects and make image smoother.

1. Chao Rui, Zhang Ke , Li Yan-jun. An image fusion algorithm using Wavelet Transform[J]. Chinese Journal of Electronics , 2004, 32(5):750-753.
2. Li, H. , Manjunath, B. S. , Mitra, S. K. , 1995. Multisensorimage fusion using the Wavelet Transform. Graphical Models Image Process. 57 (5),235–245.
3. Li, S. , Wang, Y. , 2000. Multisensor image fusion using discrete multiWavelet Transform. In: Proc. 3rd Internat. Conf. on Visual Computing, pp. 93–103.
4. Li, H. , Manjunath, B. S. , Mitra, S. K. , 1995. Multisensor image fusion using the Wavelet Transform. Graphical Models Image Process. 57 (5), 235–245.
5. E. J. Candes? D. L. Donoho. Curvelets? A surprisingly effective nonadaptive representation for objects with edges[J]. In:C. Rabut A. Cohen ? L. L. Schumaker. Curves and Surfaces. Nashville , TN: Vanderbilt University Press ? 2000. 105-120.
6. E. J. Candes, D. L. Donoho. New tight frames of curvelets and optimal representations of objects with singularities[J]. Commun. On Pure and Appl. Math. 2004, 57(2):219-266.
7. E. J. Candes , L. Demanet D. L. Donoho et al. . Fast Discrete Curvelet Transforms[R]. Applied and Computational Mathematics. California Institute of Technology , 2005. 1.
8. LiHui-hu,i GuoLe,i LiuHang. Research on image fusion based on the second generation curvelet transform [J]. Acta Optica Sinica, 2006,26(5): 657 ~662.
9. L. J. Chipman, T. M. Orr, and L. N. Lewis. Wavelets and image fusion. IEEE Transactions on Image Processing, 3:248–251.
10. P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Trans. Pattern Anal. March. Intell. , vol. 12, no. 7, pp. 629-639, Jul. 1990
11. Rudin L I, Osher S, Fatemi E Nonlinear total variation based noise removal algorithm s[J]. Physica D, 1992, 60: 259-268

Computed Tomography (ct) Magnetic Resonance Image (mri) Fusion Wavelets Pca Transform