Medical image fusion based on statistical modeling

Abstract

For improving the imaging quality and increasing the clinical applicability, a novel approach to multimodal medical image fusion is proposed based on statistical modeling in contourlet transform domain. Firstly, the coefficients of the approximate subband are modeled as Gaussian mixture distribution, and fused by a new rule using the weighted average of a posterior probability. Then, the coefficients of the detail subbands are modeled by generalized Gaussian distribution, and a selection rule is used for fusion based on the estimated parameters and the matching measure. Finally, an effectively fused image is achieved through inverse contourlet transform. The experimental results show that, compared with existing approaches, the proposed method can make the fused image have better performance, and provide more valuable diagnostic information.