Medical Image Fusion by Combining SVD and Shearlet Transform

The method of incorporating information from multiple images into a single image to get enhanced imaging quality and reduce randomness and redundancy in medical images for diagnosis and assessment of medical problems. In this paper, we present a new technique for medical image fusion using Singular Value Decomposition (SVD) method on Shearlet Transform (ST) domain to improve the information content of an image by fusing images like positron emission tomography (PET) and magnetic resonance imaging (MRI) images. The proposed method first transforms the source image into shearlet-image by using Shearlet Transform (ST). Then, we have used SVD model in low-pass sub-band and selected modified sub-bands according to their local characteristics. The composition of different high-pass sub-band coefficients are processed by ST decomposition. Then the high and the low sub-band are fused. Finally, the fused image is reconstructed by performing the inverse shearlet transform (IST). We have used three benchmark images to carry out our experiment and compare with many state-of-art techniques. Experimental results demonstrate that the proposed method outperforms many state-of-the-art techniques in both subjective and objective evaluation criteria.