Wavelet and fast discrete curvelet transform for medical application

Image fusion has great importance in many applications such as satellite imaging, remote sensing and medical imaging. The main purpose of medical image fusion is to obtain a high resolution image with as much details as possible for diagnosis of disease and better medical treatment. For medical diagnosis, MR and CT images are of main concern, both images give special sophisticated characteristics of the organ to be imaged. These two images provide complementary information. For accurate diagnosis of disease, complementary information is required from different modalities. This paper proposes fusion of magnetic resonance (MR) and computed tomography (CT) medical images using wavelet and fast discrete curvelet transform, which describe the curved shapes of images and analyses feature of images better. The results of proposed method are analyzed and compared visually and statistically with two types of wavelets used in image fusion. Simulation results proved that the proposed method can obtain a better image than the two types of wavelets used in image fusion. The proposed method can be helpful for better medical diagnosis.