Multi Model Medical Image Fusion under Non Subsampled Contour let Transform Domain.

The Project presents the multi modal medical image fusion technique based on discrete non subsampled contourlet transform and pixel level fusion rule. The fusion criterion is to minimize different error between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the interested objects is more important than other information. Therefore, how to preserve the edge-like features is worthy of investigating for medical image fusion. As we know, the image with higher contrast contains more edge-like features. In term of this view, the project proposed a new medical image fusion scheme based on discrete contourlet transformation, which is useful to provide more details about edges at curves. It is used to improve the edge information of fused image by reducing the distortion. This transformation will decompose the image into finer and coarser details and finest details will be decomposed into different resolution in different orientation. The pixel and decision level fusion rule will be applied selected for low frequency and high frequency and in these rule we are following image averaging, Gabor filter bank and gradient based fusion algorithm. The fused contourlet coefficients are reconstructed by inverse NS contourlet transformation. The visual experiments and quantitative assessments demonstrate the effectiveness of this method compared to present image fusion schemes, especially for medical diagnosis.The goal of image fusion is to obtain useful complementary information from CT/MRI multimodality images. By this method we can get more complementary information and also satisfactory Entropy, Better correlation coefficient, PSNR (Peak- Signal-to-Noise Ratio) and less MSE (Mean square error).