High-Throughput Detection of Linear Features: Selected Applications in Biological Imaging

  • Luke Domanski
  • Changming Sun
  • Ryan Lagerstrom
  • Dadong Wang
  • Leanne Bischof
  • Matthew Payne
  • Pascal Vallotton
Part of the Biological and Medical Physics, Biomedical Engineering book series (BIOMEDICAL)


Psychovisual experiments support the notion that a considerable amount of information is contained in region boundaries such as edges and linear features [1]. Thus, as long as these elements are preserved, it is possible to simplify images drastically with no apparent loss of content. Linear features also underlie the organization of many structures of interest in biology, remote sensing, medicine, and engineering. Examples include rivers and their deltas, road networks, the circulatory system, and textile microstructure (see [2] for a more extensive list and Chapters 6, 7, and 11 in this book).


Root Segment Linear Feature Small Window Size Output Pixel Primary Maximum 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the following people for allowing us to use their images as sample images: Marjo Götte, Novartis Institutes for BioMedical Research; Dr. Myles Fennell, Wyeth Research, Princeton, NJ, USA; Dr. Xiaokui Zhang, Helicon Therapeutics, Inc., USA; Prof. Pat Doherty, Kings College, London, UK; Dr. Jenny Gunnersen, Prof. Seong-Seng Tan, and Dr. Ross O’Shea Howard Florey Institute, Melbourne; Ass. Prof. Cynthia Whitchurch, UTS, Sydney.


  1. 1.
    Palmer, S.E.: Vision science. MIT, MA (1999)Google Scholar
  2. 2.
    Sun, C., Vallotton, P.: Fast linear feature detection using multiple directional non-maximum suppression. J. Microsc. 234, 147–57 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36, 81–121 (2004)CrossRefGoogle Scholar
  4. 4.
    Meijering, E.: Neuron tracing in perspective. Cytometry 77, 693–704 (2010)Google Scholar
  5. 5.
    Neubeck, A., Van Gool, L.: Efficient non-maximum suppression. In: Tang, Y.Y., Wang, S.P., Lorette, G., Yeung, D.S., Yan, H. (eds.) Proceedings of 18th International Conference on Pattern Recognition, vol. 3, pp. 850–855. IEEE Computer Soc, Los Alamitos (2006)Google Scholar
  6. 6.
    Soille, P.: Morphological image analysis, 2nd edn. Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Sun, C., Pallottino, S.: Circular shortest path in images. Pattern Recogn. 36, 709–719 (2003)CrossRefGoogle Scholar
  8. 8.
    Dorst, L., Smeulders, A.W.M.: Length estimators for digitized contours. Comput. Vis. Graph. Image Process. 40, 311–333 (1987)CrossRefGoogle Scholar
  9. 9.
    Lagerstrom, R., Sun, C., Vallotton, P.: Boundary extraction of linear features using dual paths through gradient profiles. Pattern Recogn. Lett. 29, 1753–1757 (2008)CrossRefGoogle Scholar
  10. 10.
    Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Kruger, J., Lefohn, A.E., Purcell, T.J.: A survey of general-purpose computation on graphics hardware. Comput. Graph. Forum 26, 80–113 (2007)CrossRefGoogle Scholar
  11. 11.
    Harris, M.: Optimizing parallel reduction in CUDA. NVIDIA SDK white paper (2007)Google Scholar
  12. 12.
    Hakura, Z.S., Gupta, A.: The design and analysis of a cache architecture for texture mapping. In: 24th Annual International Symposium on Computer Architecture, Conference Proceedings, pp. 108–120. Assoc Computing Machinery, New York (1997)Google Scholar
  13. 13.
    Cox, M., Bhandari, N., Shantz, M.. Multi-level texture caching for 3D graphics hardware. In: Proceedings of the 25th Annual International Symposium on Computer Architecture. IEEE Computer Soc, Los Alamitos, pp. 86–97 (1998)Google Scholar
  14. 14.
    Domanski, L.: Linear feature detection on GPUs. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney (2010)Google Scholar
  15. 15.
    Vallotton, P., Lagerstrom, R., Sun, C., Buckley, M., Wang, D., De Silva, M., Tan, S.S., Gunnersen, J.M.: Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision. Cytometry A 71, 889–895 (2007)Google Scholar
  16. 16.
    Conrad, C., Gerlich, D.W.: Automated microscopy for high-content RNAi screening. J. Cell Biol. 188, 453–461 (2010)CrossRefGoogle Scholar
  17. 17.
    Lau, C., O’Shea, R., Broberg, B., Bischof, L., Beart, P.: The Rho kinase inhibitor Fasudil up-regulates astrocytic glutamate transport subsequent to actin remodelling in murine cultured astrocytes. Br. J. Pharmacol. 163, 533–545 (2011)
  18. 18.
    Vallotton, P., Sun, C., Wang, D., Ranganathan, P., Turnbull, L. Whitchurch, C.: Segmentation and tracking of individual Pseudomonas aeruginosa bacteria in dense populations of motile cells. In: Image and Vision Computing New Zealand Wellington, New Zealand (2009)Google Scholar
  19. 19.
    Orkisz, M.M., Bresson, C., Magnin, I.E., Champin, O., Douek, P.C.: Improved vessel visualization in MR angiography by nonlinear anisotropic filtering. Magn. Reson. Med. 37, 914–919 (1997)CrossRefGoogle Scholar
  20. 20.
    Heijmans, H., Buckley, M., Talbot, H.: Path-based morphological openings. In: ICIP: 2004 International Conference on Image Processing, vol. 1–5, pp. 3085–3088. IEEE, New York (2004)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Luke Domanski
    • 1
  • Changming Sun
    • 1
  • Ryan Lagerstrom
    • 1
  • Dadong Wang
    • 1
  • Leanne Bischof
    • 1
  • Matthew Payne
    • 1
  • Pascal Vallotton
    • 1
  1. 1.CSIRO (Commonwealth Scientific and Industrial Research Organisation)North RydeAustralia

Personalised recommendations