Abstract
A multiscale representation of PET difference images is presented in order to characterize functional cerebral activations. This method is based on the linear scale-space representation of our 3-dimensional images, and derived from the scale-space primal sketch [1]. Objects are extracted at each level of scale, and linked across the scales to get objects in the whole scale-space. Several measures are associated to them and we show how these measures can be used to establish a hierarchy between these objects in order to characterize functional activations.
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References
T. Lindeberg. Scale-space theory in computer vision. Kluwer Academic, 1994.
J.B. Poline and B.M. Mazoyer. Analysis of individual brain activation maps using hierarchical description and multiscale detection. IEEE Transactions on Medical Imaging, vol. 13, no. 4, 1994, pp. 702–710.
K.J. Worsley, S. Marrett, P. Neelin, and A.C. Evans. Searching scale space for activation in PET images. Human Brain Mapping, vol. 4, 1996, pp. 74–90.
T. Lindeberg. Scale-space for n-dimensional discrete signals. In Y.O. Ying, A. Toet, D. Foster, H. Heijmanns, and P. Meer, editors, Shape in Picture: Mathematical description of shape in grey-level images, volume 126 of NATO ASI F, 1994, pp. 571–590.
O. Coulon, I. Bloch, V. Frouin, and J.-F. Mangin. Analysis of PET images using 3D scale-space primal sketch. Technical report, Ecole Nationale Supérieure des Télécommunications, 1997.
A.P. Witkin. Scale-space filtering. In International Joint Conference on Artificial Intelligence, 1983, pp. 1019–1023.
J.J. Koenderink. The structure of images. Biological Cybernetics, vol. 50, 1984, pp. 363–370.
L.M.J. Florack, B.M. ter Haar Romeny, J.J. Koenderink, and M.A. Viergever. Scale-space and the differential structure of images. Image and Vision Computing, vol. 10, 1992, pp. 376–388.
K.J. Friston. Statistical parametric mapping. In R.W. Thatcher, M. Hallet, T. Zeffiro, E.R. John, and M. Huerta, editors, Functional Neuroimaging: Technical Foundations, 1994, pp. 79–93.
L. Lifshitz and S.M. Pizer. A multiresolution hierarchical approach to image segmentation based on intensity extrema. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 6, 1990, pp. 529–540.
G. Gerig, G. Szekely, G. Israel, and M. Berger. Detection and characterization of unsharp blobs by curve evolution. In Information Processing in Medical Imaging, 1995, pp. 165–176.
K.J. Friston, K.J. Worsley, R.S.J. Frackowiak, J.C. Mazziotta, and A.C. Evans. Assessing the significance of focal activations using their spatial extent. Human Brain Mapping, vol. 1, 1994, pp. 214–220.
T. Lindeberg. Effective scale: a natural unit for measuring scale-space lifetime. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, 1993, pp. 1068–1074.
A.-L. Paradis, J.-F. Mangin, I. Bloch, V. Cornilleau-Pérès, E. Moulines, and D. Le Bihan. Detection of periodic signals in brain echo-planar functional images. In IEEE-EMBS, Amsterdam, 1996.
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© 1997 Springer-Verlag Berlin Heidelberg
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Coulon, O., Bloch, I., Frouin, V., Mangin, JF. (1997). Multiscale measures in linear scale-space for characterizing cerebral functional activations in 3D PET difference images. In: ter Haar Romeny, B., Florack, L., Koenderink, J., Viergever, M. (eds) Scale-Space Theory in Computer Vision. Scale-Space 1997. Lecture Notes in Computer Science, vol 1252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63167-4_50
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DOI: https://doi.org/10.1007/3-540-63167-4_50
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