Abstract
Microscopic cellular images segmentation has become an important routine procedure in modern biological research, due to the rapid advancement of fluorescence probes and robotic microscopes in recent years. In this paper we advocate a discriminative learning approach for cellular image segmentation. In particular, three new features are proposed to capture the appearance, shape and context information, respectively. Experiments are conducted on three different cellular image datasets. Despite the significant disparity among these datasets, the proposed approach is demonstrated to perform reasonably well. As expected, for a particular dataset, some features turn out to be more suitable than others. Interestingly, we observe that a further gain can often be obtained on top of using the “good” features, by also retaining those features that perform poorly. This might be due to the complementary nature of these features, as well as the capacity of our approach to better integrate and exploit different sources of information.
Chapter PDF
Similar content being viewed by others
References
Bengtsson, E., Wahlby, C., Lindblad, J.: Bobust cell image segmentation methods. Pattern Recognition and Image Analysis 14(2), 157–167 (2004)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE TPAMI 23(11), 1222–1239 (2001)
Cardona, A., Saalfeld, S., Preibisch, S., Schmid, B., Cheng, A., Pulokas, J., Tomancak, P., Hartenstein, V.: An integrated micro- and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol. 8 (2010)
Chen, S., Gordon, G., Murphy, R.: Graphical models for structured classification, with an application to interpreting images of protein subcellular location patterns. J. Mach. Learn. Res. 9, 651–682 (2008)
Everingham, M., Gool, L.V., Williams, C., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) challenge. Int. J. of Computer Vision 88(2), 303–338 (2010)
Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Int. Conf. Computer Vision and Pattern Recognition (2008)
Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., Siddiqi, K.: Turbopixels: Fast superpixels using geometric flows. IEEE TPAMI 31, 2290–2297 (2009)
Lezoray, O., Cardot, H.: Cooperation of color pixel classification schemes and color watershed: a study for microscopical images. IEEE TIP 11(7), 783–789 (2002)
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. IJCV 30, 117–154 (1998)
Marcuzzo, M., Quelhas, P., Campilho, A., Mendonca, A.M., Campilho, A.: Automated arabidopsis plant root cell segmentation based on svm classification and region merging. Comput. Biol. Med. 39, 785–793 (2009)
Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering object categories in image collection. In: ICCV (2005)
Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res. 6, 1453–1484 (2005)
Yu, W., Lee, H., Hariharan, S., Bu, W., Ahmed, S.: Evolving generalized voronoi diagrams of active contours for accurate cellular image segmentation. Cytometry 77, 379–386 (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
Cheng, L., Ye, N., Yu, W., Cheah, A. (2011). Discriminative Segmentation of Microscopic Cellular Images. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23623-5_80
Download citation
DOI: https://doi.org/10.1007/978-3-642-23623-5_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23622-8
Online ISBN: 978-3-642-23623-5
eBook Packages: Computer ScienceComputer Science (R0)