Image registration
From Wikipedia, the free encyclopedia
 |
This article needs additional citations for verification. Please help improve this article by adding reliable references (ideally, using inline citations). Unsourced material may be challenged and removed. (August 2008) |
In computer vision, sets of data acquired by sampling the same scene or object at different times, or from different perspectives, will be in different coordinate systems. Image registration is the process of transforming the 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.
Medical image registration (e.g. for data of the same patient taken at different points in time) often additionally involves elastic (or nonrigid) registration to cope with deformation of the subject (due to breathing, anatomical changes, etc.). Nonrigid 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.
Contents |
[edit] Algorithm classification
[edit] Intensity-based vs feature-based
Image registration algorithms can be classified into intensity-based and feature-based[1]. One of the images is referred to as the reference or source and the second image is referred to as the target or sensed. Image registration involves spatially transforming the target image to align with the reference image[1].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[1]. Intensity-based methods register entire images or subimages. If subimages are registered, centers of corresponding subimages are treated as corresponding feature points. Feature-based method established correspondence between a number of points in images. Knowing the correspondence between a number of points in images, a 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[1].
[edit] Transformation models
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 translation, rotation, scaling, and affine. Linear transformations are global in nature, thus, they cannot model local geometric differences between images[1].
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[1]), physical continuum models (viscous fluids), and large deformation models (diffeomorphisms).
[edit] Spatial vs. frequency domain methods
Spatial methods operate in the image domain, matching intensity patterns or features in images. Some of the feature matching algorithms are outgrowths of traditional techniques for performing manual image registration, in which an operator chooses corresponding control points (CPs) in images. When the number of control points exceeds the minimum required to define the appropriate transformation model, iterative algorithms like RANSAC can be used to robustly estimate the parameters of a particular transformation type (e.g. affine) for registration of the images.
Frequency-domain methods find the transformation parameters for registration of the images while working in the transform domain. Such methods work for simple transformations, such as translation, rotation, and scaling. Applying the Phase correlation method to a pair of images produces a third image which contains a single peak. The location of this peak corresponds to the relative translation between the images. Unlike many spatial-domain algorithms, the phase correlation method is resilient to noise, occlusions, and other defects typical of medical or satellite images. Additionally, the phase correlation uses the fast Fourier transform to compute the cross-correlation between the two images, generally resulting in large performance gains. The method can be extended to determine rotation and scaling differences between two images by first converting the images to log-polar coordinates. Due to properties of the Fourier transform, the rotation and scaling parameters can be determined in a manner invariant to translation.
[edit] Single- vs. multi-modality methods
Another classification can be made between single-modality and multi-modality methods. Single-modality methods tend to register images in the same modality acquired by the same scanner/sensor type, while multi-modality registration methods tended to register images acquired by different scanner/sensor types.
Multi-modality registration methods are often used in medical imaging as images of a subject are frequently obtained from different scanners. Examples include registration of brain CT/MRI images or whole body PET/CT images for tumor localization, registration of contrast-enhanced CT images against non-contrast-enhanced CT images for segmentation of specific parts of the anatomy, and registration of ultrasound and CT images for prostate localization in radiotherapy.
[edit] Automatic vs. interactive methods
Registration methods may be classified based on the level of automation they provide. Manual, interactive, semi-automatic, and automatic methods have been developed. Manual methods provide tools to align the images manually. Interactive methods reduce user bias by performing certain key operations automatically while still relying on the user to guide the registration. Semi-automatic methods perform more of the registration steps automatically but depending on the user to verify the correctness of a registration. Automatic methods do not allow any user interaction and perform all registration steps automatically.
[edit] Applications
Image registration has applications in remote sensing (cartography updating), medical imaging (change detection, tumor monitoring), and computer vision. Due to the vast applications to which image registration can be applied, it is impossible to develop a general method that is optimized for all uses.
[edit] Similarity measures for image registration
Image similarities are broadly used in medical imaging. An image similarity measure quantifies the degree of similarity between intensity patterns in two images[1]. The choice of an image similarity measure depends on the modality of the images to be registered. Common examples of image similarity measures include cross-correlation, mutual information, sum of squared intensity differences, and ratio image uniformity. Mutual information and normalized mutual information are the most popular image similarity measures for registration of multimodality images. Cross-correlation, sum of squared intensity differences and ratio image uniformity are commonly used for registration of images in the same modality.
[edit] Uncertainty
There is a level of uncertainty associated with registering images that have any spatio-temporal differences. A confident registration with a measure of uncertainty is critical for many change detection applications such as medical diagnostics.
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 such as [2] and [3] However, many approaches to quantifying uncertainty or estimating deformations are computational intensive or are only applicable to limited sets of spatial transformations.
[edit] Open Source Software
These tools meet the definition of open source.
[edit] Other Software
[edit] See also
[edit] References
- ^ a b c d e f g A. Ardeshir Goshtasby: 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications, Wiley Press, 2005.
- ^ Simonson, K., Drescher, S., Tanner, F., A Statistics Based Approach to Binary Image Registration wtih Uncertainty Analysis. IEEE Pattern Analysis and Machine Intelligence, Vol. 29, No. 1, January 2007
- ^ Domokos, C., Kato, Z., Francos, J., Parametric estimation of affine deformations of binary images. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2008
[edit] External Links
- B. Glocker, N. Komodakis, G. Tziritas, N. Navab, N. Paragios: Dense Image Registration through MRFs and Efficient Linear Programming. Medical Image Analysis, (in press), 2008.
- Barbara Zitová, Jan Flusser: Image registration methods: a survey. Image Vision Comput. 21(11): 977-1000 (2003).
- G. D. Evangelidis, E.Z. Psarakis: Parametric Image Alignment using Enhanced Correlation Coefficient Maximization. IEEE Trans. on PAMI, vol.30, no.10, 2008.
- Jan Modersitzki: Numerical Methods for Image Registration, Oxford University Press, 2004.
- W. R. Crum, Griffin LD, Hill DL, Hawkes DJ: Zen and the art of medical image registration: correspondence, homology, and quality. Neuroimage, Vol. 20, No. 3. (November 2003), pp. 1425-1437.
- Gupta Nisheeth, Gupta Nikhil: A VLSI Architecture for Image Registration in Real Time. IEEE Trans. on VLSI, Vol. 15, No. 9, Sept 2007.
