Techniques for mutual information-based brain image registration and their applications

Mutual information is a powerful similarity measure assuming no specific relationship between the intensities of corresponding points. It has been shown that for many applications, by maximizing the mutual information, two images can be aligned with high precision. To utilize mutual information as a similarity measure for image registration, the joint histogram of two images must be estimated. Under certain conditions, existing joint histogram estimation methods result in a phenomenon known as interpolation induced artifacts that hamper the subsequent optimization process and may deteriorate registration accuracy. In this chapter, we discuss this phenomenon in detail and review some plausible solutions to reduce this phenomenon. In addition, a robust global optimization scheme is introduced in this chapter to optimize the mutual information similarity measure between two images. The core of this scheme lies in a heuristic global optimum test algorithm to distinguish the global optimum of the mutual information registration function from the numerous local ones. Finally, the effectiveness of the techniques covered in this chapter is demonstrated by two clinical examples including 3D brain image registration using the data provided by Vanderbilt University and cryosection image registration using the Visible Human Project data sets from the National Library of Medicine.