Hierarchical Planar Correlation Clustering for Cell Segmentation

  • Julian Yarkony
  • Chong Zhang
  • Charless C. Fowlkes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8932)

Abstract

We introduce a novel algorithm for hierarchical clustering on planar graphs we call “Hierarchical Greedy Planar Correlation Clustering” (HGPCC). We formulate hierarchical image segmentation as an ultrametric rounding problem on a superpixel graph where there are edges between superpixels that are adjacent in the image. We apply coordinate descent optimization where updates are based on planar correlation clustering. Planar correlation clustering is NP hard but the efficient PlanarCC solver allows for efficient and accurate approximate inference. We demonstrate HGPCC on problems in segmenting images of cells.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Tan, M., Deklerck, R., Jansen, B., Bister, M., Cornelis, J.: A novel computer-aided lung nodule detection system for CT images. Medical Physics 38, 5630–5645 (2011)CrossRefGoogle Scholar
  2. Ailon, N., Charikar, M.: Fitting tree metrics: Hierarchical clustering and phylogeny. In: Proceedings of the Symposium on Foundations of Computer Science, pp. 73–82 (2005)Google Scholar
  3. Yarkony, J.: MAP Inference in Planar Markov Random Fields with. Applications to Computer Vision. PhD thesis, University of California Irvine (2012)Google Scholar
  4. Ren, X., Malik, J.: Learning a classification model for segmentation. In: Ninth IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2003), vol. 1, pp. 10–17 (October 2003)Google Scholar
  5. Liu, F., Xing, F., Yang, L.: Robust muscle cell segmentation using region selection with dynamic programming. In: Eleventh IEEE International Symposium on Biomedical Imaging (ISBI 2014), pp. 1381–1384 (2014)Google Scholar
  6. Wu, Z., Gurari, D., Wong, J.Y., Betke, M.: Hierarchical Partial Matching and Segmentation of Interacting Cells. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 389–396. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. Su, H., Yin, Z., Kanade, T., Hun, S.: Interactive cell segmentation based on correction propagation. In: Eleventh IEEE International Symposium on Biomedical Imaging (ISBI 2014), pp. 1267–1270 (2014)Google Scholar
  8. Su, H., Yin, Z., Hun, S., Kanade, T.: Cell segmentation in phase contrast microscopy images via semi-supervised classification over optics-related features. Medical Image Analysis 17, 746–765 (2013)CrossRefGoogle Scholar
  9. Zhang, C., Huber, F., Knop, M., Hamprecht, F.A.: Yeast Cell Detection and Segmentation in Bright Field Microscopy. In: Eleventh IEEE International Symposium on Biomedical Imaging (ISBI 2014), pp. 1267–1270 (2014a)Google Scholar
  10. Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Journal of Machine Learning, 238–247 (2002)Google Scholar
  11. Kim, S., Nowozin, S., Kohli, P., Yoo, C.D.: Higher-order correlation clustering for image segmentation. Advances in Neural Information Processing Systems 25, 1530–1538 (2011)Google Scholar
  12. Yarkony, J., Ihler, A., Fowlkes, C.C.: Fast Planar Correlation Clustering for Image Segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 568–581. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. Bagon, S., Galun, M.: Large scale correlation clustering 816 optimization. CoRR, abs/1112.2903 (2011)Google Scholar
  14. Andres, B., Kroeger, T., Briggman, K.L., Denk, W., Korogod, N., Knott, G., Koethe, U., Hamprecht, F.A.: Globally optimal closed-surface segmentation for connectomics. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 778–791. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. Andres, B., Yarkony, J., Manjunath, B.S., Kirchhoff, S., Turetken, E., Fowlkes, C.C., Pfister, H.: Segmenting planar superpixel adjacency graphs w.r.t. Non-planar superpixel affinity graphs. In: Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., Tai, X.-C. (eds.) EMMCVPR 2013. LNCS, vol. 8081, pp. 266–279. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  16. Andres, B., Kappes, J.H., Beier, T., Kothe, U., Hamprecht, F.A.: Probabilistic image segmentation with closedness constraints. In: Proceedings of the Fifth International Conference on Computer Vision (ICCV 2011), pp. 2611–2618 (2011)Google Scholar
  17. Bachrach, Y., Kohli, P., Kolmogorov, V., Zadimoghaddam, M.: Optimal coalition structures in graph games. CoRR, abs/1108.5248 (2011)Google Scholar
  18. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the Eighth International Conference on Computer Vision (ICCV-2001), pp. 416–423 (2001)Google Scholar
  19. Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to Detect Cells Using Non-overlapping Extremal Regions. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 348–356. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. Sommer, C., Straehle, C., Kothe, U., Hamprecht, F.A.: ilastik: Interactive learning and segmentation toolkit. In: Eighth IEEE International Symposium on Biomedical Imaging, ISBI 2011 (2011)Google Scholar
  21. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (2001)Google Scholar
  22. Zhang, C., Yarkony, J., Hamprecht, F.A.: Cell Detection and Segmentation Using Correlation Clustering. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 9–16. Springer, Heidelberg (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Julian Yarkony
    • 1
  • Chong Zhang
    • 2
  • Charless C. Fowlkes
    • 3
  1. 1.Experian Data LabSan DiegoUSA
  2. 2.CellNetworksUniversity of HiedelbergGermany
  3. 3.Department of Computer ScienceUniversity of CaliforniaIrvine

Personalised recommendations