Non-rigid image registration by using graph-cuts with mutual information

Non-rigid image registration plays an important role in medical image analysis. Recently, Tang and Chung proposed to model the non-rigid medical image registration problem as an energy minimization framework. The optimization was done by using the graph-cuts algorithm via alpha-expansions. However, the dissimilarity measure used in the energy function of this graph-cuts based method was restricted to the sum of absolute differences (SAD) and the sum of squared differences (SSD). In this paper, to utilize an advanced dissimilarity measure, such as mutual information (MI), we adopt an approximation of MI to the graph-cuts based method. Exploiting the mutual information is valuable as it can capture complex statistical relationships between the intensities of the image pair without a priori knowledge of those relationships. We have compared the proposed method against the original graph-cuts based methods, and two state-of-the-art approaches. The experimental results demonstrate that the proposed method can achieve lower registration errors.