CT and MRI image fusion based on variance and pixel significance

In the medical imaging field, images are obtained using various modalities, including the computed tomography procedure and the magnetic resonance imaging procedure. In which every image contains different information from the other image. For better treatment and diagnosis of a patient, a single composite image must be created by fusing all the pertinent data. This process is known as image fusion. We present an innovative and effective image fusion technique utilizing ILWT and DCT for combining brain-related medical images acquired through there are two steps to this strategy. First, the variance is employed as a contrast evaluation in the DCT domain to combine the approximation coefficients generated by the ILWT decomposition. Second, the coefficients representing the fine details are combined by finding the ideal weighted average based on the importance of the pixels in the ILWT domain. Our method is straightforward, making it simple and suitable for deployment in real-time applications. The experimental results demonstrate our method's outstanding performance with regard to both result quality and in contrast to a number of picture fusion techniques.