Sets of data acquired by sampling the same scene or object at different times or from different perspectives will be in different coordinate systems in image processing. Image registration is the process of transforming different sets of data into one coordinate system. Registration is necessary in order to be able to compare or integrate the data obtained from different measurements.
Image Registration is the process of estimating an optimal transformation between two images. Sometimes also known as "Spatial Normalization" (SPM).
Medical image registration (e. g. for data of the same patient taken at different points in time) often additionally involves elastic (or non-rigid) registration to cope with deformation of the subject (due to breathing, anatomical changes, etc.). Non-rigid registration of medical images can also be used to register a patient's data to an anatomical atlas, such as the Talairach atlas for neuroimaging. The below shows an example if image registration.
The process of aligning two or more images of the same scene is also known as image registration. Typically, one image, known as the base image or reference image, is used as the standard against which the other images, known as input images, are compared. The goal of image registration is to align the input image with the base image using a spatial transformation on the input image. The differences between the input and output images could be due to terrain relief and other changes in perspective when imaging the same scene from different perspectives. Distortion can also be caused by lens and other internal sensor distortions, as well as differences between sensors and sensor types. Spatial transformation maps points in one image to new points in another. The image registration process relies heavily on determining the parameters of the spatial transformation required to align the images.
Some commonly used applications of image registration are:
Motion Correction.
Correcting for Geometric Distortion in EPI.
Alignment of images obtained at different times or with different imaging parameters.
Formation of Composite Functional Maps.
Mapping of PET/SPECT to MR Images.
Atlas-based segmentation/brain stripping.
Change Detection: Images taken at different times.