Hybrid medical image fusion using wavelet and curvelet transform with multi-resolution processing

Medical image fusion combines the complementary images from different modalities and enhances the quality of fused output image that provides more anatomical and functional information to the experts. In this paper, a new hybrid approach is proposed to combine the images obtained from Computer Tomography and Magnetic Resonance Imaging using Wavelet and Curvelet Transform Techniques. Alongside, sub-band coding, scale search algorithm was also been used for filtering and image scaling operations was used for decomposition. Ridgelet Transform techniques was used along with radon Transform to perform image transition from 1-D to 2-D during reconstruction stage of fused image. The resulting image was evaluated using objective metrics of Entropy (H), Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR). The results showed that the proposed approach has resulted better information in terms of undertaken performance than the existing methods.