Abstract
Recent microscopic imaging systems such as whole slide scanners provide very large (up to 18GB) high resolution images. Such amounts of memory raise major issues that prevent usual image representation models from being used. Moreover, using such high resolution images, global image features, such as tissues, do not clearly appear at full resolution. Such images contain thus different hierarchical information at different resolutions. This paper presents the model of tiled top-down pyramids which provides a framework to handle such images. This model encodes a hierarchy of partitions of large images defined at different resolutions. We also propose a generic construction scheme of such pyramids whose validity is evaluated on an histological image application.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bister, M., Cornelis, J., Rosenfeld, A.: A critical view of pyramid segmentation algorithms. Pattern Recognition Letters 11(9), 605–617 (1990)
Brun, L., Mokhtari, M., Domenger, J.P.: Incremental modifications on segmented image defined by discrete maps. Journal of Visual Communication and Image Representation 14, 251–290 (2003)
Damiand, G., Bertrand, Y., Fiorio, C.: Topological model for two-dimensional image representation: definition and optimal extraction algorithm. Computer Vision and Image Understanding 93(2), 111–154 (2004)
Goffe, R., Brun, L., Damiand, G.: Tiled top-down combinatorial pyramids for large images representation. International Journal of Imaging Systems and Technology 21(1), 28–36 (2011)
Goffe, R., Damiand, G., Brun, L.: A causal extraction scheme in top-down pyramids for large images segmentation. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR&SPR 2010. LNCS, vol. 6218, pp. 264–274. Springer, Heidelberg (2010)
Guigues, L., Cocquerez, J.P., Le Men, H.: Scale-sets image analysis. International Journal of Computer Vision 68(3), 289–317 (2006)
Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans. on PAMI 24(7), 881–892 (2002)
Kovalevsky, V.A.: Finite topology as applied to image analysis. Computer Vision, Graphics, and Image Processing 46(2), 141–161 (1989)
Roullier, V., Lézoray, O., Ta, V.-T., Elmoataz, A.: Mitosis extraction in breast-cancer histopathological whole slide images. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Chung, R., Hammoud, R., Hussain, M., Kar-Han, T., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) ISVC 2010. LNCS, vol. 6453, pp. 539–548. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Goffe, R., Brun, L., Damiand, G. (2011). Tiled Top-Down Pyramids and Segmentation of Large Histological Images. In: Jiang, X., Ferrer, M., Torsello, A. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2011. Lecture Notes in Computer Science, vol 6658. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20844-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-642-20844-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20843-0
Online ISBN: 978-3-642-20844-7
eBook Packages: Computer ScienceComputer Science (R0)