A Bag-of-Words Model for Cellular Image Segmentation

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 120)

Abstract

Cellular segmentation in microscopy images is an important step in modern biological research. Microscopy image segmentation is known to be a difficult problem, as illustrated in the paper, in many scenarios the microscopic images become a real challenge for existing methods to accurately segment these cellular objects of interest. In this paper we propose a learning based approach using a bag-of-words model and dedicated feature design to deal with this problem. By introducing the recent machine learning and computer vision techniques including sparse coding, superpixel representation, our approach is shown to achieve good performance in practice.

Keywords

Image Segmentation Sparse Code Foreground Object Breast Cancer Dataset Score Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Batra, D., Sukthankar, R., Chen, T.: Learning Class-Specific Affinities for Image Labelling. In: Int. Conf. Computer Vision and Pattern Recognition (2008)Google Scholar
  2. 2.
    Bengtsson, E., Wahlby, C., Lindblad, J.: Robust Cell Image Segmentation Methods. Pattern Recognition and Image Analysis 14(2), 157–167 (2004)Google Scholar
  3. 3.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE TPAMI 23(11), 1222–1239 (2001)CrossRefGoogle Scholar
  4. 4.
    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(10) (2010)Google Scholar
  5. 5.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001)Google Scholar
  6. 6.
    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)MathSciNetMATHGoogle Scholar
  7. 7.
    Cheng, L., Vishwanathan, S., Schuurmans, D., Wang, S., Caelli, T.: Implicit Online Learning with Kernels. Neural Information Processing Systems (2007)Google Scholar
  8. 8.
    Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20(3), 273–297 (1995)MATHGoogle Scholar
  9. 9.
    Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A.: The Pascal Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88(2), 303–338 (2010)CrossRefGoogle Scholar
  10. 10.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A Discriminatively Trained, Multiscale, Deformable Part Model. In: Int. Conf. Computer Vision and Pattern Recognition (2008)Google Scholar
  11. 11.
    Gelasca, E., Obara, B., Fedorov, D., Kvilekval, K., Manjunath, B.: A biosegmentation benchmark for evaluation of bioimage analysis methods. BMC Bioinformatics 10, 386 (2009)CrossRefGoogle Scholar
  12. 12.
    Gonfaus, J., Boix, X., Weijer, J., Bagdanov, A., Serrat, J., Gonzalez, J.: Semantic Texton Forests for Image Categorization and Segmentation. In: Int. Conf. Computer Vision and Pattern Recognition (2010)Google Scholar
  13. 13.
    Hunter, D., Moradzadeh, A., Whitlock, E., Brenner, M., Myckatyn, T., Wei, C., Tung, T., Mackinnon, S.: Binary imaging analysis for comprehensive quantitative histomorphometry of peripheral nerve. Journal of Neuroscience Methods 166(1), 116–124 (2007)CrossRefGoogle Scholar
  14. 14.
    Kohli, P., Ladicky, L., Torr, P.: Robust Higher Order Potentials for Enforcing Label Consistency. Int. J. Comput. Vision 82(3), 302–324 (2009)CrossRefGoogle Scholar
  15. 15.
    Ladicky, L., Russell, C., Kohli, P., Torr, P.: Associative Hierarchical CRFs for Object Class Image Segmentation. In: Int. Conf. on Computer Vision (2009)Google Scholar
  16. 16.
    Lang, P., Yeow, K., Nichols, A., Scheer, A.: Cellular imaging in drug discovery. Nat. Rev. Drug. Disco. 5(4), 343–356 (2006)CrossRefGoogle Scholar
  17. 17.
    Levinshtein, A., Stere, A., Kutulakos, K., Fleet, D., Dickinson, S., Siddiqi, K.: TurboPixels: Fast Superpixels Using Geometric Flows. IEEE Trans. Pattern Anal. Mach. Intell. 31(12), 2290–2297 (2009)CrossRefGoogle Scholar
  18. 18.
    Lezoray, O., Cardot, H.: Cooperation of color pixel classification schemes and color watershed: a study for microscopical images. IEEE Transactions on Image Processing 11(7), 783–789 (2002)CrossRefGoogle Scholar
  19. 19.
    Li, K., Chen, M., Kanade, T., Miller, E., Weiss, L., Campbell, P.: Cell Population Tracking and Lineage Construction with Spatiotemporal Context. Medical Image Analysis 12(1), 546–566 (2008)CrossRefGoogle Scholar
  20. 20.
    Lindeberg, T.: Edge Detection and Ridge Detection with Automatic Scale Selection. International Journal of Computer Vision 30(2), 117–154 (1998)CrossRefGoogle Scholar
  21. 21.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Discriminative Learned Dictionaries for Local Image Analysis. In: International Conf. of Computer Vision and Pattern Recognition, CVPR (2008)Google Scholar
  22. 22.
    Marcuzzo, M., Quelhas, P., Campilho, A., Maria Mendonca, A., Campilho, A.: Automated Arabidopsis plant root cell segmentation based on SVM classification and region merging. Comput. Biol. Med. 39(9), 785–793 (2009)CrossRefGoogle Scholar
  23. 23.
    Martin, D., Fowlkes, C., Malik, J.: Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. IEEE Trans. Pattern Anal. Mach. Intell. 26(5), 530–549 (2004)CrossRefGoogle Scholar
  24. 24.
    Olhausen, B., Field, D.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)CrossRefGoogle Scholar
  25. 25.
    Pan, J., Kanade, T., Chen, M.: Heterogeneous Conditional Random Field: Realizing Joint Detection and Segmentation of Cell Regions in Microscopic Images. In: IEEE Conf. on Computer Vision and Pattern Recognition (2010)Google Scholar
  26. 26.
    Paragios, N., Deriche, R.: Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation. Int. J. Comput. Vision 46(3), 223–247 (2002)MATHCrossRefGoogle Scholar
  27. 27.
    Prewitt, J., Mendelsohn, M.: The analysis of cell images. Ann. NY Acad. Sci. 128(3), 1035–1053 (1966)CrossRefGoogle Scholar
  28. 28.
    Ren, X., Malik, J.: Learning a classification model for segmentation. In: Int’l. Conf. Computer Vision, vol. 1, pp. 10–17 (2003)Google Scholar
  29. 29.
    Serre, T., Wolf, L., Poggio, T.: Object Recognition with Features Inspired by Visual Cortex. In: International Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  30. 30.
    Shalev-Shwartz, S., Singer, Y., Srebro, N.: Pegasos: Primal Estimated sub-GrAdient Solver for SVM. In: International Conference on Machine Learning, pp. 807–814 (2007)Google Scholar
  31. 31.
    Shotton, J., Johnson, M., Cipolla, R.: Semantic Texton Forests for Image Categorization and Segmentation. In: Int. Conf. Computer Vision and Pattern Recognition (2008)Google Scholar
  32. 32.
    Shotton, J., Winn, J., Rother, C., Criminisi, A.: TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Appearance, Shape and Context. International Journal on Computer Vision 81(2) (2009)Google Scholar
  33. 33.
    Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering object categories in image collection. In: ICCV (2005)Google Scholar
  34. 34.
    Szummer, M., Kohli, P., Hoiem, D.: Learning cRFs using graph cuts. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 582–595. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  35. 35.
    Taskar, B., Guestrin, C., Koller, D.: Max-Margin Markov Networks. In: NIPS, pp. 25–32. MIT Press, Cambridge (2004)Google Scholar
  36. 36.
    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)MathSciNetMATHGoogle Scholar
  37. 37.
    Vapnik, V.: The nature of statistical learning theory. Springer, Heidelberg (1995)MATHGoogle Scholar
  38. 38.
    Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained Linear Coding for Image Classification. In: CVPR (2010)Google Scholar
  39. 39.
    Wu, X., Shah, S.: A bottom-up and top-down model for cell segmentation using multispectral data. In: IEEE International Symposium on Biomedical Imaging (2010)Google Scholar
  40. 40.
    Yang, Y., Hallman, S., Ramanan, D., Fowlkes, C.: Layered Object Detection for Multi-Class Segmentation. In: Int. Conf. Computer Vision and Pattern Recognition (2010)Google Scholar
  41. 41.
    Yang, J., Yu, K., Gong, Y., Huang, T.: Linear spatial pyramid matching using sparse coding for image classification. In: CVPR, pp. 1794–1801 (2009)Google Scholar
  42. 42.
    Yin, Z., Bise, R., Chen, M., Kanade, T.: Cell Segmentation in Microscopy Imagery using a Bag of Local Bayesian Classifiers. In: IEEE International Symposium on Biomedical Imaging, ISBI (2010)Google Scholar
  43. 43.
    Yu, K., Zhang, T., Gong, Y.: Nonlinear learning using local coordinate coding. In: Neural Information Processing Systems, NIPS (2009)Google Scholar
  44. 44.
    Yu, W., Lee, H.K., Hariharan, S., Bu, W., Ahmed, S.: Evolving Generalized Voronoi Diagrams of Active Contours for Accurate Cellular Image Segmentation. Cytometry Part A 77A(4), 379–386 (2010)CrossRefGoogle Scholar
  45. 45.
    Yu, W., Lee, H.K., Hariharan, S., Bu, W., Ahmed, S.: Quantitative Neurite Outgrowth Measurement Based on Image Segmentation with Topological Dependence. Cytometry Part A 75(4), 289–297 (2009)CrossRefGoogle Scholar
  46. 46.
    Yu, W., Lee, H.K., Hariharan, S., Sankaran, S., Vallotton, P., Ahmed, S.: Segmentation of neural stem/Progenitor cells nuclei within 3-D neurospheres. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Wang, J.-X., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5875, pp. 531–543. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.BioInformatics Institute, A*STARSingaporeSingapore
  2. 2.IMCB, A*STARSingaporeSingapore
  3. 3.NUHSSingaporeSingapore

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