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Automatic alignment of intramodal tomographic data using s-distance approach

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Języki publikacji
EN
Abstrakty
EN
The alignment of volumetric datasets is an important problem in the processing of medical data. It is a prerequisite to numerous image based applications in diagnostic and therapeutic routines. In this paper, a new method is proposed for matching of 3D intramodality medical images. Our approach is based on some generalization of feature distance definition. Analogous to the standard surface matching, our algorithm uses also the chamfer distance like metric to define the quality of match function, however, the evaluation of the distance map is performed in a different way. The s-distance method is a step towards an automatic extraction of features, where each feature’s role in the registration process is weighted based on its relative statistical or spatial significance. As an alternative to the user-dependent non-automatic registration methods this approach offers a good assessment of similarity in the intramodality case. The elimination of less significant features in the registration process has resulted in a greatly improved efficiency over the voxel-based methods. Studying certain properties of the search space topography provides some insights into the performance of the proposed method as well as the standard registration algorithms in the rigid body registration problem.
Rocznik
Tom
Strony
IT3--11
Opis fizyczny
Bibliogr. 9 poz., rys., tab.
Twórcy
autor
  • Surgical Systems Laboratory, Caesar - Center of Advanced European Studies and Research, Ludwig-Erhard-Allee 2, 53175 Bonn, Germany
Bibliografia
  • [1] MAINTZ J.B.A., VIERGEVER M.A., A survey of medical image registration, Medical Image Analysis 2(1), pp. 1-36, 1998.
  • [2] PELIZZARI C.A., CHEN G.T.Y., SPELBRING D.R., WEICHSELBAUM R.R., CHEN C.-T., Accurate three-dimensional registration of CT, PET, and/or MR images of the brain, Journal of Computer Assisted Tomography, 13(1), pp. 20-26, 1989.
  • [3] HILL D.L.G., HAWKES D.J., Medical image registration using voxel similarity measures, AAAI Spring Symposium on Computer Vision in Medical Image Processing, Stanford University, March 1994.
  • [4] COLLIGNON A., VANDERMEULEN D., SUETENS P., MARCHAL G., 3D multi-modality medical image registration using feature space clustering. In N. Ayache, editor, CVRMed’95, volume 905 of Lecture Notes in Computer Science, Springer Verlag, pp. 195-204, 1995.
  • [5] PLUIM J.P.W., MAINTZ J.B.A., VIERGEVER M.A., Mutual information based registration of medical images: a survey, IEEE Transactions on Medical Imaging, 2003, (in press)
  • [6] KRÓL Z., Computational Methods in the Registration and Visualization of Three-dimensional Multi-modality Medical Data. PhD thesis, Munich University of Technology, 1998.
  • [7] MAINTZ J.B.A., VAN DEN ELSEN P.A., VIERGEVER M.A., Comparison of edge-based and ridge-based registration of CT and MR brain images. In N. Ayache, editor, CVRMed’95, volume 905 of Lecture Notes in Computer Science, Springer Verlag , pp. 219-228, 1995.
  • [8] BORGEFORS G., Distance transformations in arbitrary dimensions, Computer Vision, Graphics and Image Processing, No. 27, pp. 321-345, 1984.
  • [9] AN LAARHOVEN P.J.M., AARTS E.H.J., Simulated Annealing: Theory and Applications. Series: Mathematics and its applications. D. Reidel Publishing Company, Dordrecht, 1987.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0020-0003
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