Advertisement

Efficient Interactive Multi-object Segmentation in Medical Images

  • Leissi Margarita Castañeda LeonEmail author
  • Paulo André Vechiatto de Miranda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11132)

Abstract

In medical image segmentation, it is common to have several complex objects that are difficult to detect with simple models without user interaction. Hence, interactive graph-based methods are commonly used in this task, where the image is modeled as a connected graph, since graphs can naturally represent the objects and their relationships. In this work, we propose an efficient method for the multiple object segmentation of medical images. For each object, the method constructs an associated weighted digraph of superpixels, attending its individual high-level priors. Then, all individual digraphs are integrated into a hierarchical graph, considering structural relations of inclusion and exclusion. Finally, a single energy optimization is performed in the hierarchical weighted digraph satisfying all the constraints and leading to globally optimal results. The experimental evaluation on 2D medical images indicates promising results comparable to the state-of-the-art methods, with low computational complexity.

Keywords

Medical image segmentation Interactive segmentation Graph-based image segmentation Superpixels segmentation 

Notes

Acknowledgments

This research is part of the FAPESP Thematic Research Project (proc. 2014/12236-1). Also, this work is part of the INCT of the Future Internet for Smart Cities funded by CNPq, proc. 465446/2014-0, CAPES proc. 88887.136422/2017-00, and FAPESP, proc. 2014/50937-1.

References

  1. 1.
    Alexandre, E.B., Chowdhury, A.S., Falcao, A.X., Miranda, P.A.V.: IFT-SLIC: a general framework for superpixel generation based on simple linear iterative clustering and image foresting transform. In: 2015 28th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 337–344 (2015)Google Scholar
  2. 2.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  3. 3.
    Delong, A., Boykov, Y.: Globally optimal segmentation of multi-region objects. In: IEEE 12th International Conference on Computer Vision, pp. 285–292 (2009)Google Scholar
  4. 4.
    Golodetz, S., Voiculescu, I., Cameron, S.: Simpler editing of graph-based segmentation hierarchies using zipping algorithms. Pattern Recognit. 70, 44–59 (2017)CrossRefGoogle Scholar
  5. 5.
    Kéchichian, R., Valette, S., Desvignes, M., Prost, R.: Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and graph cut. IEEE Trans. Image Process. 22(11), 4224–4236 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Leon, L.M.C., Ciesielski, K.C., Miranda, P.A.V.: Efficient hierarchical multi-object segmentation in layered graph (submitted). https://www.math.wvu.edu/~kcies/SubmittedPapers/SS29.HLOIFT.pdf
  7. 7.
    Leon, L.M.C., De Miranda, P.A.V.: Multi-object segmentation by hierarchical layered oriented image foresting transform. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 79–86 (2017)Google Scholar
  8. 8.
    Mansilla, L., Jackowski, M., Miranda, P.: Image foresting transform with geodesic star convexity for interactive image segmentation. In: IEEE International Conference on Image Processing (ICIP), Melbourne, Australia, pp. 4054–4058, September 2013Google Scholar
  9. 9.
    Mansilla, L.A.C., Miranda, P.A.V.: Image segmentation by oriented image foresting transform with geodesic star convexity. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013. LNCS, vol. 8047, pp. 572–579. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40261-6_69CrossRefGoogle Scholar
  10. 10.
    Miranda, P.A., Falcão, A.X.: Links between image segmentation based on optimum-path forest and minimum cut in graph. J. Math. Imaging Vis. 35(2), 128–142 (2009)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Miranda, P.A., Mansilla, L.A.: Oriented image foresting transform segmentation by seed competition. IEEE Trans. Image Process. 23(1), 389–398 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Singaraju, D., Grady, L., Vidal, R.: Interactive image segmentation via minimization of quadratic energies on directed graphs. In: IEEE Conference on Image Processing (CVPR), pp. 1–8 (2008)Google Scholar
  13. 13.
    Ulén, J., Strandmark, P., Kahl, F.: An efficient optimization framework for multi-region segmentation based on lagrangian duality. IEEE Trans. Med. Imaging 32(2), 178–188 (2013)CrossRefGoogle Scholar
  14. 14.
    Veksler, O.: Star shape prior for graph-cut image segmentation. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 454–467. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-88690-7_34CrossRefGoogle Scholar
  15. 15.
    Zhu, L., Kolesov, I., Gao, Y., Kikinis, R., Tannenbaum, A.: An effective interactive medical image segmentation method using fast growcut. In: MICCAI Workshop on Interactive Medical Image Computing (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Leissi Margarita Castañeda Leon
    • 1
    Email author
  • Paulo André Vechiatto de Miranda
    • 1
  1. 1.Institute of Mathematics and StatisticsUniversity of São PauloSão PauloBrazil

Personalised recommendations