We review the major types of registration approaches used in brain imaging today. We focus on their conceptual basis, the underlying mathematics, and their strengths and weaknesses in different contexts. We describe the major goals of registration, including data fusion, quantification of change, automated image segmentation and labeling, shape measurement, and pathology detection. We indicate that registration algorithms have great potential when used in conjunction with a digital brain atlas, which acts as a reference system in which brain images can be compared for statistical analysis. The resulting armory of registration approaches is fundamental to medical image analysis, and in a brain mapping context provides a means to elucidate clinical, demographic, or functional trends in the anatomy or physiology of the brain.
Image registration is a prerequisite to numerous imaging applications in the neurosciences.
Registration is required for three dimensional reconstruction, multimodality image mappings, atlas construction and arithmetic operations such as image averaging, subtraction and correlation. Physical sectioning procedures, unlike various tomographic imaging techniques, also require additional superpositioning schemes to register serial sections. Since the mid-1980's investigators have used several approaches, with varying degrees of manual interaction, to perform image registration. These approaches use either information obtained about the shape and topology of objects in the image, or the presumed consistency in the intensity information from one slice to its immediate neighbor, or from one brain or image set to another. Registration algorithms have a powerful range of applications when used in conjunction with a brain atlas .