抽象的な

Reduced Contrast Medical Image Estimation using Information Divergence Measure based on Iterative Algorithms

Arash Kalami

We proposed a new information divergence measure, referred to as divergence, which satisfies the requirements. A decision map is then generated by applying the divergence to measure the coherenceof sourceactivity maps at the pixel level. We further segment the decision map into two regions. It is the set of pixels whose activity patterns are similar in all the sourceimages, while it is the set of pixels whose activity patterns are different. Our fusionscheme is to find the solution for optimization problem.In fact that averaging results in reduced contrast for all the patterns which appear in only one source. On the other hand, maximum selection scheme produces some mosaic like artifacts due to the high frequency noise introduced by a sudden switch between two sets of source wavelet coefficients