Self-organizing map-based multi-thresholding on neural stem cells images

Original Article
First Online:
Received:
Accepted:

Abstract

Automatic segmentation and tracking systems can be useful tools for biologists to monitor and understand the proliferation and the differentiation of neural stem cells. This paper applied the self-organizing map-based multi-thresholding on the neural stem cells images. Using local variance as the local spatial feature and quadtree decomposition as the sub-sampling method, inner-cell regions, cell borders and background can be roughly classified. Based on these results, proper foreground and background seeds were constructed for the seeded watershed segmentation and every single cell in a cell cluster can be segmented correctly. The results were also compared to the seeded watershed segmentation based on regional maxima method.

Keywords

Self-organizing map Multi-thresholding Watershed Neural stem cells Image segmentation 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgment

This work was supported by the Shenzhen Key Laboratory Program of Health Science and Technology.

References

  1. 1.
    Althoff K, Degerman J, Gustavsson T (2005) Combined segmentation and tracking of neural stem-cells. Lect Notes Comput Sci 3540:282–291CrossRefGoogle Scholar
  2. 2.
    Althoff K, Degerman J, Gustavsson T (2005) Tracking neural stem cells in time-lapse microscopy image sequences. Proc SPIE 5747:1883–1891. doi: 10.1117/12.594767 CrossRefGoogle Scholar
  3. 3.
    Bengtsson E (2003) Computerized cell image analysis: past, present, and future. Lect Notes Comput Sci 2749:395–407CrossRefGoogle Scholar
  4. 4.
    Campos LS (2004) Neurospheres: insights into neural stem cell biology. J Neurosci Res 78:761–769. doi: 10.1002/jnr.20333 CrossRefGoogle Scholar
  5. 5.
    Gage HF (2000) Mammalian neural stem cells. Science 287:1433–1438. doi: 10.1126/science.287.5457.1433 CrossRefGoogle Scholar
  6. 6.
    Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, New JerseyMATHGoogle Scholar
  7. 7.
    Kachouie NN, Fieguth P (2005) A narrow-band level-set method with dynamic velocity for neural stem cell cluster segmentation. Lect Notes Comput Sci 3656:1006–1013CrossRefGoogle Scholar
  8. 8.
    Laywell ED, Kukekov VG, Suslov O (2002) Production and analysis of neurospheres from acutely dissociated and postmortem CNS specimens. In: Zigova T (ed) Methods in molecular biology, vol. 198: Neural stem cells: methods and protocols. Humana Press, Totowa, pp 15–28Google Scholar
  9. 9.
    Lindvall O, Kokaia Z (2006) Stem cells for the treatment of neurological disorders. Nature 441:1094–1096. doi: 10.1038/nature04960 CrossRefGoogle Scholar
  10. 10.
    Lindvall O, Kokaia Z, Martinez-Serrano A (2004) Stem cell therapy for human neurodegenerative disorders—how to make it work. Nat Med 10:S42–S50CrossRefGoogle Scholar
  11. 11.
    McKay R (1997) Stem cell in the central nervous system. Science 276:66–71. doi: 10.1126/science.276.5309.66 CrossRefGoogle Scholar
  12. 12.
    Papamarkos N (2003) A neuro-fuzzy technique for document binarisation. Neural Comput Appl 12:190–199. doi: 10.1007/s00521-003-0382-z CrossRefGoogle Scholar
  13. 13.
    Papamarkos N, Atsalakis A (2000) Gray-level reduction using local spatial features. Comput Vis Image Underst 78:336–350. doi: 10.1006/cviu.2000.0838 CrossRefGoogle Scholar
  14. 14.
    Papamarkos N, Strouthopoulos C, Andreadis I (2000) Multithresholding of color and gray-level images through a neural network technique. Image Vis Comput 18:213–222. doi: 10.1016/S0262-8856(99)00015-3 CrossRefGoogle Scholar
  15. 15.
    Pinidiyaarachchi A, Wahlby C (2005) Seeded watersheds for combined segmentation and tracking of cells. Lect Notes Comput Sci 3617:336–343CrossRefGoogle Scholar
  16. 16.
    Reddi SS, Rudin SF, Keshavan HR (1984) An optimal multiple threshold scheme for image segmentation. IEEE Trans Syst Man Cybern 14:661–665Google Scholar
  17. 17.
    Shah-Hosseini H, Safabakhsh R (2002) Automatic multilevel thresholding for image segmentation by the growing time adaptive self-organizing map. IEEE Trans Pattern Anal Mach Intell 24:1388–1393. doi: 10.1109/TPAMI.2002.1039209 CrossRefGoogle Scholar
  18. 18.
    Shusterman E, Feder M (1994) Image compression via improved quadtree decomposition algorithms. IEEE Trans Image Process 3:207–215. doi: 10.1109/83.277901 CrossRefGoogle Scholar
  19. 19.
    Tang C, Bengtsson E (2005) Segmentation track neural stem cell. Lect Notes Comput Sci 3645:851–859CrossRefGoogle Scholar
  20. 20.
    Vincent L (1993) Morphological gray scale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Process 2:176–201. doi: 10.1109/83.217222 CrossRefGoogle Scholar
  21. 21.
    Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13:583–598. doi: 10.1109/34.87344 CrossRefGoogle Scholar
  22. 22.
    Wu K, Gauthier D, Levine MD (1995) Live cell image segmentation. IEEE Trans Biomed Eng 42:1–12. doi: 10.1109/10.362924 CrossRefGoogle Scholar

Copyright information

© International Federation for Medical and Biological Engineering 2009

Authors and Affiliations

  • Xiang Qian
    • 1
    • 2
  • Cheng Peng
    • 2
  • Xueli Wang
    • 1
    • 2
  • Datian Ye
    • 1
    • 2
  1. 1.Department of Biomedical EngineeringTsinghua UniversityBeijingChina
  2. 2.Research Center of Biomedical Engineering, Graduate School at ShenzhenTsinghua UniversityShenzhenChina

Personalised recommendations