A novel technique for medical image fusion using improved contourlet transformation with modified DFBs

Medical Image fusion plays an critical and vital role in today´s medical field to diagnose particular disease and this concept is still under research. Image Fusion is a process in which we merge two or more images captured by different instruments or modalities to produce single image that provide accurate and clear information without any degradation in quality of image. Fused image will be analyses by doctors to diagnose the disease in particular patient. In this paper, we proposed novel technique which is based on contourlet transformation. We proposed improved contourlet in which pyramid decomposition is replaced by multiscale decomposition with improved DBFs: replace low pass filter and high pass filter with log Gabor filters. This novel technique not only sharply localizes image also eliminates DC component to provide accurate information which is main feature of log Gabor filter. In this paper, we are fusing CT and MRI modalities images into single image without any degradation in quality. We are considering registered images only (same size and same alignment). Performance of new technique is evaluated by quality measures like EN (entropy), STD (standard deviation), MI (mutual information), Quality index and PSNR.