This paper presents a comparative study of medical image fusion algorithms along with its performance analysis. Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images are used to fuse which form a contemporary image so as to improve the complementary and redundant information for diagnosis purpose. For this, Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Principle Component Analysis (PCA) and curvelet transform techniques are employed and its experimental results are evaluated and compared. Comparison of fusion performance is based on its root mean square error (RMSE), peak signal to noise ratio (PSNR), Mutual Information (MI) and Entropy (H). Comparison results demonstrate the achievement of better performance of fusion by using curvelet transform.