Multimodal medical image fusion under nonsubsampled contourlet transform domain

The Paper presents the multi modal medical image fusion technique based on discrete non subsamples contour let transform and pixel level fusion rule. The fusion criteria is to minimize different errors 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 investing 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 fusion scheme based on discrete contour let transformation, which is useful to provide more details about edges at curves. 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 contour lettransformation. 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. Image quality metrics can be found out by satisfactory entropy,better correlation coefficient, PSNR (Peak Signal to Noise Ratio) and less MSE (Mean Square Error).