Skip to main content


Recovery and Visualization of 3D Structure of Chromosomes from Tomographic Reconstruction Images

Article metrics


The objectives of this work include automatic recovery and visualization of a 3D chromosome structure from a sequence of 2D tomographic reconstruction images taken through the nucleus of a cell. Structure is very important for biologists as it affects chromosome functions, behavior of the cell, and its state. Analysis of chromosome structure is significant in the detection of diseases, identification of chromosomal abnormalities, study of DNA structural conformation, in-depth study of chromosomal surface morphology, observation of in vivo behavior of the chromosomes over time, and in monitoring environmental gene mutations. The methodology incorporates thresholding based on a histogram analysis with a polyline splitting algorithm, contour extraction via active contours, and detection of the 3D chromosome structure by establishing corresponding regions throughout the slices. Visualization using point cloud meshing generates a 3D surface. The 3D triangular mesh of the chromosomes provides surface detail and allows a user to interactively analyze chromosomes using visualization software.


  1. 1.

    Gardner RJ: Geometric Tomography. Cambridge University Press, Cambridge, UK; 1995.

  2. 2.

    Lerner B, Guterman H, Dinstein I: A classification-driven partially occluded object segmentation (CPOOS) method with application to chromosome analysis. IEEE Transactions on Signal Processing 1998, 46(10):2841–2847. 10.1109/78.720391

  3. 3.

    Shi H, Gader P, Li H: Parallel mesh algorithm for grid graph shortest paths with application to separation of touching chromosomes. The Journal of Supercomputing 1996, 12(1–2):69–83.

  4. 4.

    Lerner B, Levinstein M, Rosenberg B, Guterman H, Dinstein L, Romem Y: Feature selection and chromosome classification using a multilayer perceptron neural network. Proceedings of IEEE International Conference on Neural Networks, June–July 1994, Orlando, Fla, USA 6: 3540–3545.

  5. 5.

    Vidal E, Castro MJ: Classification of banded chromosomes using error-correcting grammatical interface (ECGI) and multilayer perceptron (MLP). VII National Symposium on Pattern Recognition and Image Analysis, 1997 31–36.

  6. 6.

    Keller JM, Gader P, Sjahputera O, Caldwell CW, Huang H-MT: A fuzzy logic rule-based system for chromosome recognition. Proceedings of the 8th IEEE Symposium on Computer-Based Medical Systems, June 1995, Lubbock, Tex, USA 135–132.

  7. 7.

    Imelinska C, Downes MS, Yuan W: Semi-automatic color segmentation of anatomical tissue. Computerized Medical Imaging and Graphics 2000, 24(3):173–180. 10.1016/S0895-6111(00)00017-3

  8. 8.

    Holden M, Steen E, Lundervold A: Segmentation and visualization of brain lesions in multispectral magnetic resonance images. Computerized Medical Imaging and Graphics 1995, 19(2):171–183. 10.1016/0895-6111(94)00031-7

  9. 9.

    Yan J, Zhuang T-G, Schwartz LH, Zhou B: Lymph node segmentation from CT images using fast marching method. Computerized Medical Imaging and Graphics 2004, 28(1–2):33–38. 10.1016/j.compmedimag.2003.09.003

  10. 10.

    Noordmans HJ, Smeulders AWM: Detection and characterization of isolated and overlapping spots. Computer Vision and Image Understanding 1998, 70(1):23–35. 10.1006/cviu.1998.0604

  11. 11.

    Qingsong Z, Keong KC, Sing NW: Interactive surgical planning using context based volume visualization techniques. Proceedings of IEEE International Conference on Information Visualization 2002, November 2002 323–330.

  12. 12.

    Banvard RA: The visible human project® image data set from inception to completion and beyond. Proceedings of CODATA 2002: Frontiers of Scientific and Technical Data, September–October 2002, Montral, Canada

  13. 13.

    Subramanian KR, Thubrikar MJ, Fowler B, Mostafavi MT, Funk MW: Accurate 3D reconstruction of complex blood vessel geometries from intravascular ultrasound images: in vitro study. Journal of Medical Engineering and Technology 2000, 24(4):131–140. 10.1080/03091900050163391

  14. 14.

    Arnison MR, Cogswell CJ, Smith NI, Fekete PW, Larkin KG: Using Hilbert transforms for 3D visualization of differential interference contrast microscope images. Journal of Microscopy 2000, 199(1):79–84. 10.1046/j.1365-2818.2000.00706.x

  15. 15.

    Engelhardt P, Ruokolainen J, Dulenc A, Överstedt LG, Mehlin H, Skoglund U: 3D-reconstruction by electron tomography (EMT) of whole-mounted DNA-depleted metaphase chromosomes show scaffolding macro coils, 30-nm fibers and 30-nm particles. Proceedings of International Conference on 3D Image Processing in Microscopy, April 1994, Munich, Germany

  16. 16.

    Liu J, Udupa JK, Odhner D, Hackney D, Moonis G: A system for brain tumor volume estimation via MR imaging and fuzzy connectedness. Computerized Medical Imaging and Graphics 2005, 29(1):21–34. 10.1016/j.compmedimag.2004.07.008

  17. 17.

    Zoroofi RA, Sato Y, Nishii T, Sugano N, Yoshikawa H, Tamura S: Automated segmentation of necrotic femoral head from 3D MR data. Computerized Medical Imaging and Graphics 2004, 28(5):267–278. 10.1016/j.compmedimag.2004.03.004

  18. 18.

    Viergever MA, Maintz JBA, Niessen WJ, et al.: Registration, segmentation, and visualization of multimodal brain images. Computerized Medical Imaging and Graphics 2001, 25(2):147–151. 10.1016/S0895-6111(00)00065-3

  19. 19.

    Levin D, Aladl U, Germano G, Slomka P: Techniques for efficient, real-time, 3D visualization of multi-modality cardiac data using consumer graphics hardware. Computerized Medical Imaging and Graphics 2005, 29(6):463–475.

  20. 20.

    Shaw P, Agard D, Hiraoka Y, Sedat J: Tilted view reconstruction in optical microscopy: three-dimensional reconstruction of Drosophila melanogaster embryo nuclei. Biophysical Journal 1989, 55(1):101–110. 10.1016/S0006-3495(89)82783-3

  21. 21.

    Jain R, Kasturi R, Schunck BG: Machine Vision. MIT Press and McGraw-Hill, Boston, Mass, USA; 1995.

  22. 22.

    Kass M, Witkin A, Terzopoulos D: Snake: actve contour models. Proceedings of 1st International Conference on Computer Vision, 1987 259–269.

  23. 23.

    Hoover A, Jean-Baptiste G, Jiang X, et al.: An experimental comparison of range image segmentation algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 1996, 18(7):673–689. 10.1109/34.506791

  24. 24.

    Remondino F: From point cloud to surface: the modeling and visualization problem. Proceedings of International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, in International Workshop on Visualization and Animation of Reality-based 3D Models, February 2003, Tarasp-Vulpera, Switzerland

  25. 25.

    McInerney T, Terzopoulos D: Deformable models in medical image analysis: a survey. Medical Image Analysis 1996, 1(2):91–108. 10.1016/S1361-8415(96)80007-7

  26. 26.

    Wilcoxon F: Individual comparisons by ranking methods. Biometrics 1945, 1: 80–83. 10.2307/3001968

  27. 27.

    Wei M, Zhou Y, Wan M: A fast snake model based on non-linear diffusion for medical image segmentation. Computerized Medical Imaging and Graphics 2004, 28(3):109–117. 10.1016/j.compmedimag.2003.12.002

  28. 28.

    Williams DJ, Shah M: A fast algorithm for active contours and curvature estimation. Image Understanding 1992, 55(1):14–26. 10.1016/1049-9660(92)90003-L

Download references

Author information

Correspondence to Sabarish Babu.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Babu, S., Liao, P., Shin, M.C. et al. Recovery and Visualization of 3D Structure of Chromosomes from Tomographic Reconstruction Images. EURASIP J. Adv. Signal Process. 2006, 045684 (2006) doi:10.1155/ASP/2006/45684

Download citation


  • Point Cloud
  • Chromosomal Abnormality
  • Active Contour
  • Triangular Mesh
  • Chromosome Structure