Fast Fusion of Medical Images Based on Bayesian Risk Minimization and Pixon Map

Fast fusion of multiple registered out-of-focus images is of great interest in medical imaging; for example, the thoracic cavity is always too bumpy to be focused on all parts at one shot even when we can omit the unavoidable hardware vibrations. Previous proposed methods in this field cannot fulfill the real-time requirement in our multiple camera medical imaging setting. In this paper, we propose a multi-resolution Bayesian risk minimization based method to fuse these chest cavity images. The validity and efficiency of our method are verified by our experiments on both out-of-focus medical images and regional motion blurred images. By choosing special kernel functions for the Pixon map and adopting uniform distribution as the prior probability, our method can be applied to the real-time medical imaging situations such as surgical operation monitoring

Fast Fusion of Medical Images Based on Bayesian Risk Minimization and Pixon Map

Fast fusion of multiple registered out-of-focus images is of great interest in medical imaging; for example, the thoracic cavity is always too bumpy to be focused on all parts at one shot even when we can omit the unavoidable hardware vibrations. Previous proposed methods in this field cannot fulfill the realtime requirement in our multiple camera medical imaging setting. In this paper, we propose a multiresolution Bayesian risk minimization based method to fuse these chest cavity images. The validity and efficiency of our method are verified by our experiments on both out-of-focus medical images and regional motion blurred images. By choosing special kernel functions for the Pixon map and adopting uniform distribution as the prior probability, our method can be applied to the real-time medical imaging situations such as surgical operation monitoring