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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Legg, P.; Rosin, P. (2013)
Publisher: Elsevier
Languages: English
Types: Article
Subjects:

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Mutual information (MI) is a popular similarity measure for performing image registration between different modalities. MI makes a statistical comparison between two images by computing the entropy from the probability distribution of the data. Therefore, to obtain an accurate registration it is important to have an accurate estimation of the true underlying probability distribution. Within the statistics literature, many methods have been proposed for finding the ‘optimal’ probability density, with the aim of improving the estimation by means of optimal histogram bin size selection. This provokes the common question of how many bins should actually be used when constructing a histogram. There is no definitive answer to this. This question itself has received little attention in the MI literature, and yet this issue is critical to the effectiveness of the algorithm. The purpose of this paper is to highlight this fundamental element of the MI algorithm. We present a comprehensive study that introduces methods from statistics literature and incorporates these for image registration. We demonstrate this work for registration of multi-modal retinal images: colour fundus photographs and scanning laser ophthalmoscope images. The registration of these modalities offers significant enhancement to early glaucoma detection, however traditional registration techniques fail to perform sufficiently well. We find that adaptive probability density estimation heavily impacts on registration accuracy and runtime, improving over traditional binning techniques.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] P. A. Viola and W. M. Wells III. Alignment by maximization of mutual information. In ICCV, pages 16-23, 1995.
    • [2] A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens, and G. Marchal. Automated multimodality medical image registration using information theory. In Proc. 14th Int. Conf. Information Processing in Medical Imaging; Computational Imaging and Vision 3, pages 263-274, 1995.
    • [3] C. Studholme, D. L. G. Hill, and D. J. Hawkes. An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition, 32(1):71-86, 1999.
    • [4] C. E. Shannon. A mathematical theory of communication. Bell System Technical Journal, 27:379-423, 623-656, 1948.
    • [5] C. Y. Mardin, F. K. Horn, J. B. Jonas, and W. M. Budde. Preperimetrix glaucoma diagnosis by confocal scanning laser tomography of the optic disc. British Journal of Ophthalmology, 83:299-304, 1999.
    • [6] J. P. W. Pluim, J. B. Antoine Maintz, and M. A. Viergever. Mutual information based registration of medical images: A survey. IEEE Trans. Med. Imaging, 22(8):986-1004, 2003.
    • [7] J. Beirlant, E. J. Dudewicz, L. Gy¨orfi, and E. C. Meulen. Nonparametric entropy estimation: An overview. International Journal of the Mathematical Statistics Sciences, 6:17-39, 1997.
    • [8] L. Paninski. Estimation of entropy and mutual information. Neural Computation, 15:1191-1254, 2003.
    • [9] G. Egnal. Image registration using mutual information. Technical report, University of Pennsylvania, 1999.
    • [10] L. Birg´e and Y. Rozenholc. How many bins should be put in a regular histogram. Technical report, Universit´e Paris VI, UMR CNRS 7599, Universit´e du Maine, 2002.
    • [11] F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens. Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging, 16(2):187-198, 1997.
    • [12] N. D. H. Dowson and R. Bowden. A unifying framework for mutual information methods for use in non-linear optimisation. In ECCV (1), pages 365-378, 2006.
    • [13] R. Lachner. From Nano to Space. Springer Berlin Heidelberg, 2008.
    • [14] N. Ritter, R. A. Owens, J. R. Cooper, R. H. Eikelboom, and P. P. Van Saarloos. Registration of stereo and temporal images of the retina. IEEE Trans. Med. Imaging, 18(5):404-418, 1999.
    • [15] H. Nam, R. A. Renaut, K. Chen, H.Guo, and G. E. Farin. Improved inter-modality image registration using normalized mutual information with coarse-binned histograms. Communications in Numerical Medthods in Engineering, 25:583-595, 2009.
    • [16] J. Kang, C. Xiao, M. Deng, J. Yu, and H. Liu. Image registration based on harris corner and mutual information. In Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on, volume 7, pages 3434 -3437, aug. 2011.
    • [17] Y. Zhu and S. M. Cocho↵. Influence of implementation parameters on registration of MR and SPECT brain images by maximization of mutual information. Journal of Nuclear Medicine, 43(2):160-166, 2002.
    • [18] J. Tsao. Interpolation artifacts in multimodality image registration based on maximization of mutual information. IEEE Trans. Med. Imaging, 22(7):854-864, July 2003.
    • [19] P. L. Davies, U. Gather, D. Nordman, and H. Weinert. Constructing a regular histogram - a comparison of methods. Technical report, Technical University Eindhoven, 1997.
    • [20] H. A. Sturges. The choice of a class interval. American Statistical Association, pages 65-66, 1926.
    • [21] D. W. Scott. On optimal and data-based histograms. Biometrika, 66(3):605-610, 1979.
    • [22] R. J. Hyndman. The problem with Sturges' rule for constructing histograms. Technical report, Melbourne University, Australia, 1995.
    • [23] D. Freedman and P. Diaconis. On the histogram as a density estimator. Zeitschrift fur Wahrscheinlichkeitstheorie und verwandte Gebiete, 57(4):453-476, 1981.
    • [24] L. Devroye and L. Gy¨orfi. Nonparametric density estimation: The L1 view. Journal of the Royal Statistical Society. Series A (General)., 148(4):392-393, 1985.
    • [25] C. C. Taylor. Akaike's information criterion and the histogram. Biometrika, 74:636-639, 1987.
    • [26] D. P. Doane. Aesthetic frequency classifications. The American Statistician, 30(4):181-183, November 1976.
    • [27] D. W. Scott. Multivariate density estimation: theory, practice and visualization. John Wiley and Sons, 1992.
    • [28] J. D. Wichard, R. Kuhne, and A. ter Laak. Binding site detection via mutual information. Proc. of the IEEE World Congress on Computational Intelligence, pages 1770-1776, June 2008.
    • [37] N. D. H. Dowson, R. Bowden, and T. Kadir. Image template matching using mutual information and NP-windows. In ICPR (2), pages 1186- 1191, 2006.
    • [38] A. Rajwade, A. Banerjee, and A. Rangarajan. Continuous image representations avoid the histogram binning problem in mutual information based image registration. In ISBI, pages 840-843, 2006.
    • [39] Heidelberg Engineering, Heidelberg, Germany. Quantitative Threedimensional Imaging of the Posterior Segment with the Heidelberg Retina Tomograph, 1999.
    • [40] W. K. Pratt. Digital Image Processing. Wiley, New York, 2 edition, 1991.
    • [41] J. Vandekerckhove. General simulated annealing algorithm (http://www.mathworks.com/matlabcentral/fileexchange/10548), MATLAB central file exchange, June 2008.
    • [42] P. A. Legg. Multimodal retinal imaging: Improving accuracy and efficiency of image registration using Mutual Information. PhD thesis, School of Computer Science and Informatics, Cardi↵ University, 2010.
    • [43] L. Paninski. Estimating entropy on m bins given fewer than m samples. IEEE Trans. on Information Theory, 50(9):2200-2203, September 2004.
    • [44] L. Kubecka, M. Skokan, and J. Jan. Registration of bimodal retinal images - improving modifications. Engineering in Medicine and Biology Society, 2003. IEMBS '03. 25th Annual International Conference of the IEEE, 1:599-602, September 2003.
    • [45] P. A. Legg, P. L. Rosin, D. Marshall, and J. E. Morgan. A robust solution to multi-modal image registration by combining mutual information with multi-scale derivatives. In MICCAI, volume 1, pages 616-623, 2009.
    • [46] J. P. W. Pluim, J. B. Antoine Maintz, and M. A. Viergever. Image registration by maximization of combined mutual information and gradient information. IEEE Trans. Med. Imaging, 19(8):809-814, 2000.
    • [47] D. Rueckert, M. J. Clarkson, D. L. G. Hill, and D. J. Hawkes. Non-rigid registration using higher-order mutual information. Medical Imaging: Image Processing, pages 438-447, 2000.
    • [48] D. B. Russako↵, C. Tomasi, T. Rohlfing, and C. R. Maurer Jr. Image similarity using mutual information of regions. In ECCV (3), pages 596-607, 2004.
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