The Visual Computer

, Volume 27, Issue 12, pp 1055–1069 | Cite as

Automated cell classification and visualization for analyzing remyelination therapy

  • Koel Das
  • Aditi Majumder
  • Monica Siegenthaler
  • Hans Keirstead
  • M. Gopi
Original Article


Remyelination therapy is a state-of-the-art technique for treating spinal cord injury (SCI). Demyelination—the loss of myelin sheath that insulates axons, is a prominent feature in many neurological disorders resulting in SCI. This lost myelin sheath can be replaced by remyelination. In this paper, we propose an algorithm for efficient automated cell classification and visualization to analyze the progress of remyelination therapy in SCI. Our method takes as input the images of the cells and outputs a density map of the therapeutically important oligodendrocyte-remyelinated axons (OR-axons) which is used for efficacy analysis of the therapy. Our method starts with detecting cell boundaries using a robust, shape-independent algorithm based on iso-contour analysis of the image at progressively increasing intensity levels. The detected boundaries of spatially clustered cells are then separated using the Delaunay triangulation based contour separation method. Finally, the OR-axons are identified and a density map is generated for efficacy analysis of the therapy. Our efficient automated cell classification and visualization of remyelination analysis significantly reduces error due to human subjectivity. We validate the accuracy of our results by extensive cross-verification by the domain experts.


Cell detection Geometric processing Progressive isocontour 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aguado, A., Nixon, M., Montiel, M.: Parameterizing arbitrary shapes via Fourier descriptors for evidence-gathering extraction. Comput. Vis. Image Underst. 69, 202–221 (1998) CrossRefGoogle Scholar
  2. 2.
    Amenta, N., Bern, M., Eppstein, D.: The crust and the β-skeleton: Combinatorial curve reconstruction. Graph. Models Image Process. 60(2), 125–135 (1998) CrossRefGoogle Scholar
  3. 3.
    Angulo, J., Flandrin, G.: Automated detection of working area of peripheral blood smears using mathematical morphology. Anal. Cell. Pathol. 25(1), 37–49 (2003) Google Scholar
  4. 4.
    Blakemore, W., Keirstead, H.: The origin of remyelinating cells in the central nervous system. J. Neuroimmunol. 98, 69–76 (1999) CrossRefGoogle Scholar
  5. 5.
    Blight, A.: Cellular morphology of chronic spinal cord injury in the cat: analysis of myelinated axons by line-sampling. Neuroscience 10, 521–543 (1983) CrossRefGoogle Scholar
  6. 6.
    Cahn, R., Poulsen, R., Toussaint, G.: Segmentation of cervical cell images. J. Histochem. Cytochem. 25(7), 681–688 (1977) CrossRefGoogle Scholar
  7. 7.
    Caselles, V., Catte, F., Coll, T., Dibos, F.: A geometric model for active contours in image processing. Numer. Math. 66(4), 1–31 (1993) MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Chari, D., Blakemore, W.: New insights into remyelination failure in multiple sclerosis: implications for glial cell transplantation. Mult. Scler. 8, 271–277 (2002) CrossRefGoogle Scholar
  9. 9.
    Das, K., Majumder, A., Siegenthaler, M., Keirstead, H., Gopi, M.: Automated analysis of remyelination therapy for spinal cord injury. In: Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP ’10, pp. 314–321. ACM, New York (2010) CrossRefGoogle Scholar
  10. 10.
    Daul, C., Graebling, P., Hirsch, E.: From the hough transform to a new approach for the detection and approximation of elliptical arcs. Comput. Vis. Image Underst. 72, 215–236 (1998) CrossRefGoogle Scholar
  11. 11.
    Doraiswamy, H., Natarajan, V.: Efficient algorithms for computing Reeb graphs. Comput. Geom., Theory Appl. 42(6–7), 606–616 (2009) MathSciNetzbMATHGoogle Scholar
  12. 12.
    Garrido, A., de la Blanca, N.P.: Applying deformable templates for cell image segmentation. Pattern Recognit. 33(5), 821–832 (2000) CrossRefGoogle Scholar
  13. 13.
    Goto, T., Hoshino, Y.: Electrophysiological, histological, and behavioral studies in a cat with acute compression of the spinal cord. J. Orthop. Sci. 6, 59–67 (2001) CrossRefGoogle Scholar
  14. 14.
    Guy, J., Ellis, E.A., Kelley, K., Hope, G.M.: Spectra of G-ratio, myelin sheath thickness, and axon and fiber diameter in the guinea pig optic nerve. J. Comp. Neurol. 287, 446–454 (1989) CrossRefGoogle Scholar
  15. 15.
    Gyulassy, A., Bremer, P.-T., Hamann, B., Pascucci, V.: A practical approach to Morse-Smale complex computation: Scalability and generality. IEEE Trans. Vis. Comput. Graph. 14 (2008) Google Scholar
  16. 16.
    Hagyard, D., Razaz, M., Atkin, P.: Analysis of watershed algorithms for greyscale images. In: ICIP, vol. III, pp. 41–44 (1996) Google Scholar
  17. 17.
    Herzberg, A.J., Kerns, B.J., Pollack, S.V., Kinney, R.B.: DNA image cytometry of keratoacanthoma and squamous cell carcinoma. J. Invest. Dermatol. 97, 495–500 (1991) CrossRefGoogle Scholar
  18. 18.
    Jones, T., Carpenter, A., Golland, P.: Voronoi-based segmentation of cells on image manifolds. In: ICPR, vol. 2, pp. 286–289 (2002) Google Scholar
  19. 19.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes—active contour models. Int. J. Comput. Vis. 1–4, 321–331 (1987) Google Scholar
  20. 20.
    Keirstead, H.: Stem cells for the treatment of myelin loss. Trends Neurosci. 28, 677–683 (2005) CrossRefGoogle Scholar
  21. 21.
    Li, Y., Field, P., Raisman, G.: Death of oligodendrocytes and microglial phagocytosis of myelin precede immigration of Schwann cells into the spinal cord. J. Neurocytol. 28, 417–427 (1999) CrossRefGoogle Scholar
  22. 22.
    Liu, L., Sclaroff, S.: Medical image segmentation and retrieval via deformable models. In: Proc. International Conference on Image Processing, Oct. 7–10, vol. 3, pp. 1071–1074 (2001) Google Scholar
  23. 23.
    Malpica, N., de Solórzano, C.O., Vaquero, J.J., Santos, A., Vallcorba, I., Garc’ıa-Sagredo, J.M., del Pozo, F.: Applying watershed algorithms to the segmentation of clustered nuclei. Cytometry 28(4), 289–297 (1997) CrossRefGoogle Scholar
  24. 24.
    McTigue, D., Horner, P., Strokes, B., Gage, F.: Neurotrophin-3 and brain-derived neurotrophic factor induce oligodendrocyte proliferation and myelination of regenerating axons in the contused adult rat spinal cord. J. Neurosci. 18, 5354–5365 (1998) Google Scholar
  25. 25.
    Meyer, J., Velasco, K., Gopi, M.: Tracking of oligodendrocyte remyelinated axons in spinal cords. In: AIChE (2008). (Poster) Google Scholar
  26. 26.
    Milnor, J.: Morse Theory. Princeton University Press, Princeton (1969) Google Scholar
  27. 27.
    Najman, L., Schmitt, M.: Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18, 1163–1173 (1996) CrossRefGoogle Scholar
  28. 28.
    Park, J., Keller, J.: Snakes on the watershed. IEEE Trans. Pattern Anal. Mach. Intell. 23(10), 1201–1205 (2001) CrossRefGoogle Scholar
  29. 29.
    Prineas, J.: Pathology of the early lesion in multiple sclerosis. Hum. Pathol. 6, 531–534 (1975) CrossRefGoogle Scholar
  30. 30.
    Watershed, B.S.: hierarchical segmentation and waterfall algorithm. In: Mathematical Morphology and its Applications to Image Processing, pp. 69–76 (1994) Google Scholar
  31. 31.
    Salgado-Ceballos, H., Guizar-Sahagun, G., Feria-Velasco, A., Grijalva, I., Espitia, L., Ibarra, A., Madrazo, I.: Spontaneous long-term remyelination after traumatic spinal cord injury in rats. Brain Res. 782, 126–135 (1998) CrossRefGoogle Scholar
  32. 32.
    Schnorrenberg, F., Pattichis, C., Kyriacou, K., Schizas, C.: Computer-aided detection of breast cancer nuclei. IEEE Trans. Inf. Technol. Biomed. 1(2), 128–140 (1997) CrossRefGoogle Scholar
  33. 33.
    Scolding, N., Franklin, R.: Remyelination in demyelinating disease. Baillière’s Clin. Neurol. 6, 525–548 (1997) Google Scholar
  34. 34.
    Strangel, M., Hartung, H.: Remyelinating strategies for the treatment of multiple sclerosis. Prog. Neurobiol. 68, 361–376 (2002) CrossRefGoogle Scholar
  35. 35.
    Thiran, J.-P., Macq, B.: Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Trans. Biomed. Eng. 43(10), 1011–1020 (1996) CrossRefGoogle Scholar
  36. 36.
    Totoiu, M., Keirstead, H.: Spinal cord injury is accompanied by chronic progressive demyelination. J. Comp. Neurol. 486, 373–383 (2005) CrossRefGoogle Scholar
  37. 37.
    Tsai, D.: An improved generalized hough transform for the recognition of overlapping objects. Image Vis. Comput. 15, 877–888 (1997) CrossRefGoogle Scholar
  38. 38.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004) CrossRefGoogle Scholar
  39. 39.
    Waxman, S.: Demyelination in spinal cord injury. J. Neurol. Sci. 91, 1–14 (1989) CrossRefGoogle Scholar
  40. 40.
    Waxman, S.: Demyelination in spinal cord injury and multiple sclerosis: what can we do to enhance functional recovery? J. Neurotrauma 9, S105–117 (1992) Google Scholar
  41. 41.
    Wu, D., Zhang, Q.: A novel approach for cell segmentation based on directional information. In: ICBBE 2007, July 2007, pp. 920–923 (2007) Google Scholar
  42. 42.
    Wu, H.S., Barba, J., Gil, J.: Iterative thresholding for segmentation of cells from noisy images. J. Microsc. 197(3), 296–304 (2000) CrossRefGoogle Scholar
  43. 43.
    Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Trans. Image Process. 7(3), 359–369 (1998) MathSciNetzbMATHCrossRefGoogle Scholar
  44. 44.
    Zhou, X., Cao, X., Perlman, Z., Wong, S.T.C.: A computerized cellular imaging system for high content analysis in monastrol suppressor screens. J. Biomed. Inform. 39(2), 115–125 (2006) CrossRefGoogle Scholar
  45. 45.
    Zimmer, C., Labruyere, E., Meas-Yedid, V., Guillen, N., Olivo-Marin, J.-C.: Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing. IEEE Trans. Med. Imaging 21(10), 1212–1221 (2002) CrossRefGoogle Scholar
  46. 46.
    Zimmer, C., Labruyere, E., Meas-Yedid, V., Guillen, N., Olivo-Marin, J.-C.: Improving active contours for segmentation and tracking of motile cells in videomicroscopy. In: Computer Vision for Biomedical Image Applications, vol. 3765 (2005). (Poster) Google Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Koel Das
    • 1
  • Aditi Majumder
    • 2
  • Monica Siegenthaler
    • 3
  • Hans Keirstead
    • 4
  • M. Gopi
    • 2
  1. 1.South Asian UniversityDelhiIndia
  2. 2.Department of Computer ScienceUniversity of California, IrvineIrvineUSA
  3. 3.California Stem Cell, IncIrvineUSA
  4. 4.Department of Anatomy and NeurobiologyUniversity of California, IrvineIrvineUSA

Personalised recommendations