Fast Fusion of Medical Images Based on Bayesian Risk Minimization and Pixon Map
Document Type
Conference Proceeding
Publication Date
12-3-2009
Abstract
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.
DOI
10.1109/CSE.2009.59
Montclair State University Digital Commons Citation
Zhou, Hongbo; Cheng, Qiang; and Zargham, Mehdi, "Fast Fusion of Medical Images Based on Bayesian Risk Minimization and Pixon Map" (2009). Department of Computer Science Faculty Scholarship and Creative Works. 284.
https://digitalcommons.montclair.edu/compusci-facpubs/284