Image Fusion Using Pulsed Coupled Neural Network with Modified Spatial Frequency based on NSCT

There are vast Image Fusion (IF) procedures are being projected in many ways by different analysts. Each coordinate point, it means pixel-level spatial space IF strategies, for the most part, lead to differentiate decrease. Pyramidal IF plans with the end goal like Laplacian, inclination, discriminate, quantity of-low-pass, and the morphological pyramids, and so forth abandonment to existing any spatial direction selectivity in the disintegration technique, also later regularly source obstructive impacts. The generally applied Discrete Wavelet Transform (DWT) can keep ghostly statistics efficiently, however, it will not be able to communicate spatial attributes adequately. Thus, DWT based grouping tactics will not be able to save the striking highlights of input images effectively and present antiques and irregularities in the intertwined outcomes. As of late, a limited Multiscale Geometric Analysis (MGA) instruments were grown, for illustration, Curvelet, Contourlet, NSCT, and so on which don’t experience the ill effects of the issues of wavelet. There are many IF and MIF techniques that work dependent on MGA devices were likewise projected. It is hard for the doctor to analyze the malady from the MRI and CT image independently for the patient. So in our proposed work, the MRI and CT clinical image are converted into a 3-D image for a better finding of the illness influenced part progressively precise and clear.