Abstract
In image registration it is vital to perform matching of those points in a pair of images which actually match each other, and to postpone those which do not match. It is not always known in advance, however, which points have their counterparts, and where are they located. To overcome this, we propose to use the Hausdorff distance function modified by using a voting scheme as a fitting quality function. This known function performs very well in guiding the matching process and supports stable matches even for low quality data. It also makes it possible to speed up the algorithms in various ways. An application to accuracy assessment of oncological radiotherapy is presented. Low contrast of images used to perform this task makes this application a challenging test.
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References
Aaltonen-Brahme, P., Brahme, A. et al.: Specification of dose delivery in radiation therapy. Acta Oncologica, 36(Supplementum 10) (1977)
Borgefors, G.: Distance transformations in digital images. Vision, Graph., and Image Proc. 34(3) (1986) 344–371
Borgefors, G.: Hierarchical chamfer matching: A parametric edge matching algorithm. IEEE Trans. PAMI 10(6) (1988) 849–865
Gottesfeld Brown, L.: A survey of image registration techniques. ACM Computing Surveys 24(4) (1992) 325–376
Cai, J., Chu, J.C.H., Saxena, A., Lanzl, L.H.: A simple algorithm for planar image registration in radiation therapy. Med. Phys. 25(6) (1998) 824–829
Cai, J., Zhou, S-Q., Hopkinson, J., Saxena, A.V., Chu, J.: Alignment of multi-segmented anatomical features from radiation therapy images by using least squares fitting. Med. Phys. 23(13) (1996) 2069–2075
Chetverikov, D., Khenokh, Y.: Matching for shape defect detection. Proc. Conf. Computer Analysis of Images and Patterns (CAIP’99, Ljubljana, Slovenia). Lecture Notes on Computer Science, vol. 1689. Springer Verlag (Sept 1999) 367–374
Danielsson, P.E.: Euclidean distance mapping. Graph. and Image Proc. 14 (1980) 227–248
Eilertsen, K., Skretting, A., Tennvassas, T.L.: Methods for fully automated verification of patient set-up in external beam radiotherapy with polygon shaped fields. Phys. Med. Biol. 39 (1994) 993–1012
Gilhuijs, K.G.A., El-Gayed, A.A.H., van Herk, M., Vijlbrief, R.E.: An algorithm for automatic analysis of portal images: clinical evaluation for prostate treatments. Radiotherapy and Oncology 29 (1993) 261–268
Gilhuijs, K.G.A., van Herk, M.: Automatic on-line inspection of patient setup in radiation therapy using digital portal images. Med. Phys. 20(3) (1993) 667–677
Giraud, L.M., Pouliot, J., Maldague, X., Zaccarin, A.: Automatic setup deviation measurements with electronic portal images for pelvic fields. Med. Phys. 25(7) (1998) 1180–1185
Arrige, S.R., Lester, H.: A survey of hierarchical non-linear medical image registration. Pattern Recognition 32 (1999) 129–149
Huttenlocher, D.P., Rucklidge, W.J.: A multi-resolution technique for comparing images using the Hausdorff distance. Proc. IEEE Conf. on Computer Vision and Pattern Recognition. New York (Jun 1993) 705–706
Kozińska, D., Tretiak, O.J., Nissanov, J., Oztruk, C.: Multidimensional alignment using the Euclidean distance transform. Graphical Models and Image Proc., 59(6) (1997) 373–387
Mount, D.M., Netanyahu, N.S., Le Moigne, J.: Efficient algorithms for robust feature matching. Pattern Recognition 32 (1999) 17–38
Rosenfeld, A., Pfaltz, J.: Distance functions on digital pictures. Pattern Recognition 1 (1968) 33–61
Rucklidge, W.J.: Efficiently locating objects using the Hausdorff distance. Int. J. Comput. Vision 24(3) (1997) 251–270
van Elsen, P.A., Pol, E.J.D., Viergever, M.A.: Medical image matching-a review with classification. ACM Computing Surveys 24(4) (1992) 325–376
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Gut, P., Chmielewski, L., Kukołowicz, P., Dłbrowski, A. (2001). Edge-Based Robust Image Registration for Incomplete and Partly Erroneous Data. In: Skarbek, W. (eds) Computer Analysis of Images and Patterns. CAIP 2001. Lecture Notes in Computer Science, vol 2124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44692-3_38
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DOI: https://doi.org/10.1007/3-540-44692-3_38
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