Advertisement

A Modified Segmentation Approach for Overlapping Elliptical Objects with Various Sizes

  • Guanghui Zhao
  • Xingyan Zi
  • Kaitai Liang
  • Panyi Yun
  • Junwei ZhouEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10232)

Abstract

Segmentation of elliptical objects has many real-world applications including morphology analysis on biological cell, material particles and other objects which need quantitative analysis according to size and shape. However, overlapping and varying in size may make the objects segmentation extremely challenging. In this paper, a modified segmentation approach for overlapping objects with different sizes is proposed. Specifically, we extract all the concave points for each connected region from the object’s silhouette. We next fit all of the circles by two adjacent concave points and an arbitrary point which is on the edge right between the two concave points. A radius set is extracted from all the circles, and a segments set is determined by the edge fragments between all the two adjacent concave points. Based on the radius set and the segments set, we can determine if there is a large gap in the radius set and the length of segments corresponding to the radius. The edge segments and radius set are divided into two subsets, while the appropriate radius range and threshold are selected respectively from the two subsets to execute the Bounded Erosion-Fast Radial Symmetry transform to get the seed point for each object. Our experiments are taken under synthetic and real datasets, in which the overlapping objects in these datasets are with different size. The experimental outcomes show that the proposed approach outperforms other existing schemes.

Keywords

Segmentation Bounded erosion-fast radial symmetry transform Overlapping elliptical objects Convex objects 

Notes

Acknowledgement

The work described in this paper was supported in part by the National Natural Science Foundation of China [grant number 61601337], by the Fundamental Research Funds for the Central Universities (Grant No. WUT: 2017IVB025) and by Key Project of Nature Science Foundation of Hubei Province [grant number ZRZ2015000393].

References

  1. 1.
    Kothari, S., Chaudry, Q., Wang, M.D.: Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques. In: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 795–798 (2009)Google Scholar
  2. 2.
    Zhang, W.-H., Jiang, X., Liu, Y.-M.: A method for recognizing overlapping elliptical bubbles in bubble image. Pattern Recogn. Lett. 33(12), 1543–1548 (2012)CrossRefGoogle Scholar
  3. 3.
    Park, C., Huang, J.Z., Ji, J.X., Ding, Y.: Segmentation, inference and classification of partially overlapping nanoparticles. IEEE Trans. Pattern Anal. Mach. Intell. 35(3), 669–681 (2013)Google Scholar
  4. 4.
    Nehl, C.L., Liao, H., Hafner, J.H.: Optical properties of star-shaped gold nanoparticles. Nano Lett. 6(4), 683–688 (2006)CrossRefGoogle Scholar
  5. 5.
    Wang, Z.L., Petroski, J.M., Green, T.C., EI-Sayed, M.A.: Shape transformation and surface melting of cubic and tetrahedral platinum nanocrystals. J. Phys. Chem. B 102(32), 6145–6151 (1998)CrossRefGoogle Scholar
  6. 6.
    Pan, Y., Neuss, S., Leifert, A., Fischler, M., Wen, F., Simon, U., Schmid, G., Brandau, W., Jahnen-Dechent, W.: Size-dependent cytotoxicity of gold nanoparticles. Small 3(11), 1941–1949 (2007)CrossRefGoogle Scholar
  7. 7.
    Zafari, S., Eerola, T., Sampo, J., Kälviäinen, H., Haario, H.: Segmentation of overlapping elliptical objects in silhouette images. IEEE Trans. Image Process. 24(12), 5942–5952 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Husain, R.A., Zayed, A.S., Ahmed, W.M., Elhaji, H.S.: Image segmentation with improved watershed algorithm using radial bases function neural networks. In: 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, pp. 121–126 (2015)Google Scholar
  9. 9.
    Wdowiak, M., Slodkowska, J., Markiewicz, T.: Cell segmentation in desmoglein-3 stained specimen microscopic images using GVF and watershed algorithm. In: 2016 17th International Conference Computational Problems of Electrical Engineering, pp. 1–3 (2016)Google Scholar
  10. 10.
    Shen, P., Qin, W., Yang, J., Hu, W., Chen, S., Li, L., Wen, T., Gu, J.: Segmenting multiple overlapping nuclei in histopathology images based on an improved watershed. In: 2015 IET International Conference on Biomedical Image and Signal Processing, pp. 1–4 (2015)Google Scholar
  11. 11.
    Browet, A., De Vleeschouwer, C., Jacques, L., Mathiah, N., Saykali, B., Migeotte, I.: Cell segmentation with random ferns and graph-cuts. In: 2016 IEEE International Conference on Image Processing, pp. 4145–4149 (2016)Google Scholar
  12. 12.
    Zhang, L., Kong, H., Chin, C.T., Liu, S., Wang, T., Chen, S.: Automated segmentation of abnormal cervical cells using global and local graph cuts. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), pp. 485–488 (2014)Google Scholar
  13. 13.
    Wu, P., Yi, J., Zhao, G., Huang, Z., Qiu, B., Gao, D.: Active contour-based cell segmentation during freezing and its application in cryopreservation. IEEE Trans. Biomed. Eng. 62(1), 284–295 (2015)CrossRefGoogle Scholar
  14. 14.
    Zafari, S., Eerola, T., Sampo, J., Kälviäinen, H., Haario, H.: Segmentation of partially overlapping nanoparticles using concave points. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Pavlidis, I., Feris, R., McGraw, T., Elendt, M., Kopper, R., Ragan, E., Ye, Z., Weber, G. (eds.) ISVC 2015. LNCS, vol. 9474, pp. 187–197. Springer, Cham (2015). doi: 10.1007/978-3-319-27857-5_17 CrossRefGoogle Scholar
  15. 15.
    He, X.C., Yung, N.H.C.: Curvature scale space corner detector with adaptive threshold and dynamic region of support. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 791–794 (2004)Google Scholar
  16. 16.
    Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 959–973 (2003)CrossRefzbMATHGoogle Scholar
  17. 17.
    Andreev, S.: Russia - the leader of the scientific revolution. why whisper? https://regnum.ru/news/innovatio/2165960.html/
  18. 18.
    NANO-LAB. Nickel nanoparticles. http://www.nano-lab.com/nanoparticles.html/
  19. 19.
    Malpica, N., de Solorzano, C.O., Vaquero, J.J.: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 28(4), 289–297 (1997)CrossRefGoogle Scholar
  20. 20.
    Bengtsson, E., Wahlby, C., Lindblad, J.: Robust cell image segmentation methods. Pattern Recogn. Image Anal. 14(2), 157–167 (2004)Google Scholar
  21. 21.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)CrossRefGoogle Scholar
  22. 22.
    Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the mumford and shah model. Int. J. Comput. Vis. 50(3), 271–293 (2002)CrossRefzbMATHGoogle Scholar
  23. 23.
    Pereira, C.S., Fernandes, H., Mendonça, A.M., Campilho, A.: Detection of lung nodule candidates in chest radiographs. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 170–177. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72849-8_22 CrossRefGoogle Scholar
  24. 24.
    Schmitt, O., Hasse, M.: Morphological multiscale decomposition of connected regions with emphasis on cell clusters. Comput. Vis. Image Underst. 113(2), 188–201 (2009)CrossRefGoogle Scholar
  25. 25.
    Parvin, B., Yang, Q., Han, J., Chang, H., Rydberg, B., Barcellos-Hoff, M.H.: Iterative voting for inference of structural saliency and characterization of subcellular events. IEEE Trans. Image Process. 16(3), 615–623 (2007)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Guanghui Zhao
    • 1
  • Xingyan Zi
    • 1
  • Kaitai Liang
    • 2
  • Panyi Yun
    • 1
  • Junwei Zhou
    • 1
    Email author
  1. 1.Computer Science and TechnologyWuhan University of TechnologyWuhanPeople’s Republic of China
  2. 2.School of Computing, Mathematics and Digital TechnologyManchester Metropolitan UniversityManchesterUK

Personalised recommendations