Research On Medical Image Registration Based On Mutual Information

With the rapid development of imaging technology, there are more and more different modality medical images in application fields. Medical image registration has been applied to the diagnosis of breast cancer, cardiac studies, and a variety of neurological disorders including brain tumors. A new approach called maximization of mutual information (MMI), quantifying the similarity between corresponding voxel intensities of two images, has been demonstrated to be a very powerful criterion for three-dimensional medical image registration. MMI can be thought of as a measure of how well one image explains the other, it is maximized at the optimal alignment of the images. Though MMI has been widely used, many implementation issues, such as multi-resolution, interpolation, probability distribution estimation, capture ranges, and optimization methods, are still under investigation.This thesis aims to present a review of recent as well as classic image registration methods. Image registration is the process of overlaying images (two or more) of the same scene taken at different times, from different viewpoints, and/or by different sensors. The registration geometrically align two images (the reference and sensed images). The reviewed approaches are classified according to their nature (area-based and feature-based) and according to four basic steps of image registration procedure: feature detection, feature matching, mapping function design, and image transformation and resampling. Main contributions, advantages, and drawbacks of the methods are mentioned in the paper. Problematic issues of image registration and outlook for the future research are discussed too. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas.Maximum mutual information(MMI) computes the overlap area of two images. When two images have different resolutions, the higher resolution the images have, the better result it turns out ,but it will consumes much more time. Then we normalized the mutual information, which testified an overlap invariant measure. So we use normalized mutual information (NMI) as a new similarity measure. After theoretical and experimental analyses we draw a conclusion it's a better method than MMI in multi-modality image registration. The value of mutual information (MI) doesn't increase or decline in one direction at translation from one integer to the nearest integer.