Image registration

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Image registration is the process of transforming different sets of data into one coordinate system. Data may be multiple photographs, data from different sensors, times, depths, or viewpoints.[1] It is used in computer vision, medical imaging,[2] military automatic target recognition, and compiling and analyzing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.

Algorithm classification[edit]

Intensity-based vs feature-based[edit]

Image registration or image alignment algorithms can be classified into intensity-based and feature-based.[3] One of the images is referred to as the reference or source and the others are respectively referred to as the target, sensed or subject images. Image registration involves spatially registering the target image(s) to align with the reference image.[3] Intensity-based methods compare intensity patterns in images via correlation metrics, while feature-based methods find correspondence between image features such as points, lines, and contours.[3] Intensity-based methods register entire images or sub-images. If sub-images are registered, centers of corresponding sub images are treated as corresponding feature points. Feature-based methods establish a correspondence between a number of especially distinct points in images. Knowing the correspondence between a number of points in images, a geometrical transformation is then determined to map the target image to the reference images, thereby establishing point-by-point correspondence between the reference and target images.[3]

Transformation models[edit]

Image registration algorithms can also be classified according to the transformation models they use to relate the target image space to the reference image space. The first broad category of transformation models includes linear transformations, which include rotation, scaling, translation, and other affine transforms.[4] Linear transformations are global in nature, thus, they cannot model local geometric differences between images.[3]

The second category of transformations allow 'elastic' or 'nonrigid' transformations. These transformations are capable of locally warping the target image to align with the reference image. Nonrigid transformations include radial basis functions (thin-plate or surface splines, multiquadrics, and compactly-supported transformations[3]), physical continuum models (viscous fluids), and large deformation models (diffeomorphisms).

There is a number of programs that implement both estimation and application of a warp-field. It is a part of the SPM and AIR programs.

Transformations of coordinates via the law of function composition rather than addition[edit]

Alternatively, many advanced methods for spatial normalization are building on structure preserving transformations homeomorphisms and diffeomorphisms since they carry smooth submanifolds smoothly during transformation. Diffeomorphisms are generated in the modern field of Computational Anatomy based on flows since diffeomorphisms are not additive although they form a group, but a group under the law of function composition. For this reason, flows which generalize the ideas of additive groups allow for generating large deformations that preserve topology, providing 1-1 and onto transformations. Computational methods for generating such transformation are often called LDDMM[5][6][7][8] which provide flows of diffeomorphisms as the main computational tool for connecting coordinate systems corresponding to the geodesic flows of Computational Anatomy.

There are a number of programs which generate diffeomorphic transformations of coordinates via diffeomorphic mapping including MRI Studio[9] and MRI[10]

Spatial vs frequency domain methods[edit]



Single- vs multi-modality methods[edit]



Automatic vs interactive methods[edit]


Similarity measures for image registration[edit]


Many new features have been derived for cost functions based on matching methods via large deformations have emerged in the field Computational Anatomy including Measure matching which are pointsets or landmarks without correspondence, Curve matching and Surface matching via mathematical currents and varifolds.



In remote sensing applications where a digital image pixel may represent several kilometers of spatial distance (such as NASA's LANDSAT imagery), an uncertain image registration can mean that a solution could be several kilometers from ground truth. Several notable papers have attempted to quantify uncertainty in image registration in order to compare results.[13][14] However, many approaches to quantifying uncertainty or estimating deformations are computationally intensive or are only applicable to limited sets of spatial transformations.


Registration of two MRI images of the brain





See also[edit]


  1. ^ Lisa Gottesfeld Brown, A survey of image registration techniques (abstract), ACM Computing Surveys archive, volume 24, issue 4, December 1992), pages 325 - 376
  2. ^ biological imaging and brain mapping
  4. ^
  5. ^ Toga, Arthur W. (1998-11-17). Brain Warping. Academic Press. ISBN 9780080525549. 
  6. ^ "Landmark matching on brain surfaces via large deformation diffeomorphisms on the sphere — University of Utah". Retrieved 2016-03-21. 
  7. ^ "Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms". ResearchGate. doi:10.1023/B:VISI.0000043755.93987.aa. Retrieved 2016-03-21. 
  8. ^ Joshi, S. C.; Miller, M. I. (2000-01-01). "Landmark matching via large deformation diffeomorphisms". IEEE transactions on image processing: a publication of the IEEE Signal Processing Society. 9 (8): 1357–1370. doi:10.1109/83.855431. ISSN 1057-7149. PMID 18262973. 
  9. ^  Missing or empty |title= (help)
  10. ^  Missing or empty |title= (help)

External links[edit]