Abstract
In medical imaging arises the problem of matching two images of the same objects but after movements or slight deformations. We present a new method for a primitive global transformation and an improvement of a recent matching strategy which makes it more robust. The strategy consists of two steps. We consider the grey level function (modulo a normalization) as a probability density function. First, we apply a density based clustering method in order to obtain a tree which classifies the points on which the grey level function is defined. Secondly, we use the identification of the hierarchical representations of the two images to guide the image matching. The general transformation invariance properties of the representations permit to extract invariant image points. But in addition, we design a new robust coarse to fine identification of the trees which applies an implicit error measure in a prediction – correction scheme using thin plate splines to interpolate the transformation function in a finer way at each step. Therefore, we will find the correspondence between invariant points even if these have locally moved. The method’s results for matching and motion analysis on real images will be discussed.
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Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. PAMI 14, 239–256 (1992)
Bookstein, F.: Principal wraps: Thin-plate splines and the decomposition of deformations. IEEE Trans. PAMI 11, 567–585 (1989)
Gaal, S.A.: Point set topology. Academic Press, New York (1964)
Hanusse, P., Guillotaud, P.: Sémantique des images par analyse dendronique. In: AFCET, 8th RFIA, Lyon, vol. 2, pp. 577–588 (1992)
Hartigan, J.A.: Statistical theory in clustering. J. of Classification 2, 63–76 (1985)
Horn, B., Shunck, B.G.: Determining optical flow. Art. Intel. 17, 185–203 (1981)
Kok-Wiles, S.L.: Comparing mammogram pairs for the detection of mammographic lesions. PhD thesis, Brasenose College, University of Oxford (May 1998)
Kok-Wiles, S.L., Brady, J.M., Highnam, R.: Comparing mammogram pairs for the detection of lesions. In: Karssemeijer, N. (ed.) 4th Int. Workshop of Digital Mammography, Nijmegen, Netherlands. Kluwer, Amsterdam (1998)
Leu, J.G., Huang, I.N.: Planar shape matching based on binary tree shape representation. Pattern Recognition 21, 607–622 (1988)
Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2, 1–36 (1998)
Mattes, J., Demongeot, J.: Dynamic confinement, classification, and imaging. In: 22nd Annual Conference of the Gesellschaft für Klassifikation e.V., Dresden, Germany (March 1998); Studies in Classification, Data Analysis, and Knowledge Organization, pp. 205–214. Springer, Heidelberg (1999)
Mattes, J., Demongeot, J.: Statistical invariants and structures for image matching and representation. IEEE Trans. Medical Imaging (submitted)
Mattes, J., Richard, M., Demongeot, J.: Tree representation for image matching and object recognition. In: Bertrand, G., Couprie, M., Perroton, L. (eds.) DGCI 1999. LNCS, vol. 1568, pp. 298–309. Springer, Heidelberg (1999)
Pavlidis, T.: Analysis of set patterns. Pattern Recognition 1 (1968)
Thirion, J.-P.: New feature points based on geometric invariants for 3D image registration. International Journal of Computer Vision 18, 121–137 (1996)
Wishart, D.: Mode analysis: A generalization of the nearest neighbor which reduces chaining effects. In: Cole, A.J. (ed.) Numerical Taxonomy, pp. 282–319. Academic Press, London (1969)
Zhang, K., Statman, R., Shasha, D.: On the editing distance between unordered labeled trees. Information Processing Letters 42, 133–139 (1992)
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Mattes, J., Demongeot, J. (1999). Tree Representation and Implicit Tree Matching for a Coarse to Fine Image Matching Algorithm. In: Taylor, C., Colchester, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI’99. MICCAI 1999. Lecture Notes in Computer Science, vol 1679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10704282_70
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DOI: https://doi.org/10.1007/10704282_70
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