Abstract
Mutual information (MI) was independently proposed in 1995 by two groups of researchers (Maes and Collignon of Catholic University of Leuven (Collignon et al. 1995) and Wells and Viola of MIT (Viola and Wells 1995)) as a similarity measure for intensity based registration of images acquired from different types of sensors. Since its introduction, MI has been used widely for a variety of applications involving image registration. These include medical imaging (Holden et al. 2000; Maes et al. 1997; Studhilme et al. 1997; Wells et al. 1996), remote sensing (Chen et al. 2003ab), and computer vision (Chen and Varshney 2001a). The MI registration criterion states that an image pair is geometrically registered when the mutual information between the two images reaches its maximum. The strength of MI as a similarity measure lies in the fact that no assumptions are made regarding the nature of the relation between the intensity values of the image, as long as such a relationship exists. Thus, the MI criterion is very general and has been used in many image registration problems in a range of applications.
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References
Chen H (2002) Mutual information based image registration with applications. PhD dissertation, Syracuse University
Chen H, Varshney PK (2000a) A pyramid approach for multimodality image registration based on mutual information. Proceedings of 3rd international conference on information fusion 1, pp MoD3 9–15
Chen H, Varshney PK (2002) Registration of multimodal brain images: some experimental results. Proceedings of SPIE Conference on Sensor Fusion: Architectures, Algorithms, and Applications 6, Orlando, FL, 4731, pp 122–133
Chen H, Varshney PK (2001a) Automatic two stage IR and MMW image registration algorithm for concealed weapon detection. IEE Proceedings, vision, image and signal processing 148 (4): 209–216
Chen H, Varshney PK (2001b) A cooperative search algorithm for mutual information based image registration. Proceedings of SPIE Conference on Sensor Fusion: Architectures, Algorithms, and Applications 5, Orlando, FL, 4385 117–128, 2001.
Chen H, Varshney PK (2003) Mutual information based CT-MR brain image registration using generalized partial volume joint histogram estimation. IEEE Transactions on medical imaging 22 (9): 1111–1119
Chen H, Varshney PK, Arora MK (2003a) Mutual information based image registration for remote sensing data. International Journal of Remote Sensing 24 (18): 3701–3706
Chen H, Varshney PK, Arora MK (2003b) Automated registration of multi-temporal remote sensing images using mutual information. IEEE Transactions on Geoscience and Remote Sensing 41 (11): 2445–2454
Collignon A, Maes F, Delaere D, Vandermeulen D, Suetens P, Marchal G (1995) Automated multimodality medical image registration using information theory. Proceedings of 14th International Conference on Information Processing in Medical Imaging (IPMI’95), Ile de Berder, France, pp. 263–274.
Cover TM, Thomas JA (1991) Elements of information theory, John Wiley and Sons, New York.
Farsaii B, Sablauer A (1998) Global cost optimization in image registration using Simulated Annealing. Proceedings of SPIE conference on mathematical modeling and estimation techniques in computer vision 3457, San Diego, California, pp 117–125
Holden M, Hill DLG, Denton ERE, Jarosz JM, Cox TCS, Rohlfing T, Goodey J, Hawkes DJ (2000) Voxel similarity measures for 3-D serial MR brain image registration. IEEE Transactions on Medical Imaging 19 (2): 94–102.
Keys RG (1981) Cubic convolution interpolation for digital image processing. IEEE Transactions on Acoustics, Speech and Signal Processing 29 (6): 1153–1160
Lehmann TM, Gonner C, Spitzer K (1999) Survey: interpolation methods in medical image processing. IEEE Transactions on Medical Imaging 18: 1049–1075
Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16 (2): 187–197
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs, 3rd and extended edition. Springer, New York.
Pluim JPW, Maintz IBA, Viergever MA (2000) Interpolation artifacts in mutual information- based image registration. Computer vision and image understanding 77: 211–232
Studholme C, Hill DLG, Hawkes DJ (1997) Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. Medical Physics 24 (1): 25–35
Unser M, Aldroubi A, Eden M (1993) B-spline signal processing: Part I-theory. IEEE Transactions on Signal Processing 41 (2): 821–833
Unser M, Aldroubi A, Eden M (1993) B-spline signal processing: Part II-efficient design. IEEE Transactions on Signal Processing 41 (2): 834–848
Viola P, Wells III WM (1995) Alignment by maximization of mutual information. Proceed- ings of 5th International Conference on Computer Vision, Cambridge, MA, pp. 16–23
Wells WM, Viola P, Atsumi H, Nakajima S (1996) Multi-modal volume registration by maximization of mutual information. Medical Image Analysis 1 (1): 35–51
West J, et al (1997) Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of Computer Assisted Tomography 21 (4): 554–566
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Chen, Hm. (2004). Mutual Information: A Similarity Measure for Intensity Based Image Registration. In: Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-05605-9_4
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DOI: https://doi.org/10.1007/978-3-662-05605-9_4
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