Medical image fusion involves combining of multimodal sensor images to obtain both the spatial and spectral data to be used by radiologists for the purpose of disease diagnosis, monitoring research. This paper provides a comparative analysis of multiple fusion techniques that can be used to obtain accurate information from the multimodal images. The source images are initially decomposed using Discrete Wavelet Transform (DWT) into low frequency and high frequency components. This paper also provides a comparative study of the different types of DWT techniques available for decomposition. These low and high frequency components are fused using the different fusion rules. Final fused image is obtained by inverse transformation. Various performance parameters are evaluated to compare the fusion rules and rule which provides better result is analyzed. The comparison is done on the basis of which method provides the fused image with more mutual information more mutual information and high peak signal to noise ratio at minimum root mean square error. Conclusion of the comparison provides a better approach to be used for future research. © 2017 Association for Computing Machinery.