The term fusion means in general an approach to extraction of information acquired in several domains. The goal of image fusion (IF) is to integrate complementary multi sensor, multi temporal and/or multi view information into one new image containing information the quality of which cannot be achieved otherwise. This paper presents a Curvelet based approach for the fusion of magnetic resonance image (MRI) and computed tomography (CT) image. Since medical images have several objects and curved shapes, it is expected that the Curvelet transform would be better in their fusion. Fusion of images taken at different resolutions, intensity and by different techniques helps physicians to extract the features that may not be normally visible in a single image by different modalities. The objective of the fusion of an MRI image and a CT image of the same organ is to obtain a single image containing as much information as possible about that organ for diagnosis. This fused image can significantly benefit medical diagnosis and also the further image processing such as, Visualization (colorization), segmentation, classification and computeraided diagnosis. The simulation results show the visual quality of the fused image using Curvelet transform and their Entropy, Root Mean Square Error (RMSE) and the Peak Signal to Noise Ratio (PSNR).
