Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels

Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) defined on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.

I. Introduction

Deformable registration of MR brain images is the process of finding a three-dimensional (3-D) transformation that maps one individual brain image to another [1]–[45]. It is used frequently for anatomical segmentation and labeling, for morphological analysis using shape transformations, and for spatial normalization of structural and functional data. Although a large portion of the related literature focuses on models of template deformation based either on statistical models or on physical principles, image intensities or other low-level attributes (e.g., edge information) drive the registration most frequently. In this paper, we devote our effort to design richer attributes for determining voxel correspondences between same modality MR brain images of different subjects, which could be subsequently combined with various deformation models for deformable registration.