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processes and aging. Nonrigid registration algorithms will be reliable enough for clinical applications and provide valuable tools for diagnosis and for monitoring disease progression.

13.1.6 Combination of Registration and Segmentation

Good segmentation can be achieved by lining up images to an atlas using the image registration algorithm. Labeled structures in the atlas can then be used to split up the images into anatomical and pathological components for visualization or quantification. For example, registration and segmentation of plaque images may allow the detecting of much of more subtle changes.

13.1.7 Registration to Data Acquisition

Registration is beginning to be used to improve data acquisition. For example, online registration can be used to dynamically adjust slice position in MR scans to compensate prospectively for motion correction. Spectroscopic or perfusion acquisitions can be defined to interrogate specific tissues of interest, delineated in a previously acquired high resolution image, rather than a fixed region relative to the scanner. Specific tissue regions could be followed as the patient moves or is repositioned. These applications are likely to grow as algorithms become faster and scanner computing power increases.

13.2A Multiresolution Approach to Medical Image Registration Using Binning Technique

Medical image registration has been applied to the diagnosis of cardiac studies and a variety neurological disorders including brain tumors. Image registration, or alignment, is the process of aligning two images so that corresponding features of the images can be easily related. Registration using different modalities is widely used in many medical applications. In practice, the complementary information acquired from the registration of multiple imaging modalities can be used for medical diagnosis and treatment planning. In recent years many registration algorithms for medical imaging have been designed. Typically an

algorithm falls into one of three categories: algorithms that use a landmarkbased method, algorithms that use a surface-based method, or algorithms that work directly on the image intensities (voxel-based). Maintz et al. [1] gave a survey of registration methods.

For automated registration a quality measure of the registration is necessary in order to find an optimal registration. Maximization of mutual information (MI) of voxel intensities, the registration method independently proposed by Wells et al. [2] and Maes et al. [3], is one of the most popular registration methods for three-dimensional multimodality medical image registration. This method measures the statistical dependence between the image intensities of corresponding voxels in two images; this statistical dependence is maximal when the images are totally aligned.

Intensity-based methods regard all voxels in the images as independent, thus, anatomical information is not taken into consideration. Maurer et al. [4] and Gall et al. [5] exploited landmark-based methods. Audette et al. [6] gave an overview on surface registration techniques. Landmark-based methods and surface-based methods utilize features extracted from the images. The required preprocessing is usually time-consuming and the accuracy of the registration is dependent on the correctness of the landmark or surface extraction.

We have developed a two-stage method, which is both feature-based and intensity-based. Three binning methods were utilized and the performance of each is compared in this chapter. In the first stage, we segment the images using region-growing. Then we perform one of the three binning methods on the full foreground before the down-sampled images are registered. In the second stage, the results from the first stage are taken as the starting point for the registration of the full original images. Experiments show that this new two-stage method gives improved accuracy without loss of speed, compared to multiresolution registration without bin preprocessing. Of the three binning techniques used, the nonlinear binning method gave the best performance. Normalized mutual information (NMI) is used as the similarity measure, and downhill simplex method is taken as the optimization method due to its quickness in practice.

13.2.1 Image Registration Using Binning Technique

Registration based on maximization of mutual information uses an iterative ap-

proach in which an initial estimate of the transformation is gradually refined.