Iterative block level principal component averaging medical image fusion

Image fusion is a method of integrating all relevant and complementary information from images of same source or various sources into a single composite image without any degradation. In this paper, a novel pixel level fusion called Iterative block level principal component averaging fusion is proposed by dividing source images into smaller blocks, thus principal components are calculated for relevant block of source images. Average of principal components of all the blocks provide weights for fusion rule, thus importance is given to blocks of source images. In this scenario, Iterations are incorporated in the form of size of blocks of source images which gives fusion results with maximum average mutual information. This algorithm is experimented for the fusion of noise free medical images and noise filtered of the same. The experimental results for both the cases show that the proposed algorithm performs well in terms of average mutual information and mean structural similarity index.