, Volume 13, Issue 2, pp 153–166 | Cite as

Adaptive Image Enhancement for Tracing 3D Morphologies of Neurons and Brain Vasculatures

  • Zhi Zhou
  • Staci Sorensen
  • Hongkui Zeng
  • Michael Hawrylycz
  • Hanchuan Peng
Original Article


It is important to digitally reconstruct the 3D morphology of neurons and brain vasculatures. A number of previous methods have been proposed to automate the reconstruction process. However, in many cases, noise and low signal contrast with respect to the image background still hamper our ability to use automation methods directly. Here, we propose an adaptive image enhancement method specifically designed to improve the signal-to-noise ratio of several types of individual neurons and brain vasculature images. Our method is based on detecting the salient features of fibrous structures, e.g. the axon and dendrites combined with adaptive estimation of the optimal context windows where such saliency would be detected. We tested this method for a range of brain image datasets and imaging modalities, including bright-field, confocal and multiphoton fluorescent images of neurons, and magnetic resonance angiograms. Applying our adaptive enhancement to these datasets led to improved accuracy and speed in automated tracing of complicated morphology of neurons and vasculatures.


Adaptive image enhancement Anisotropic filtering Gray-scale distance transformation 3D neuron reconstruction Vaa3D 



We thank Rafael Yuste for providing samples of mouse pyramidal neurons, Paloma Gonzalez-Bellido for providing the dragonfly confocal images, Peter Kochunov for providing the MRA brain images, and Brian Long for comments. This work is supported by Allen Institute for Brain Science.


  1. Agaian, S. S., Silver, B., & Panetta, K. A. (2007). Transform coefficient histogram-based image enhancement algorithms using contrast entropy. Image Processing, IEEE Transactions on, 16(3), 741–758.CrossRefGoogle Scholar
  2. Al-Kofahi, K. A., Lasek, S., Szarowski, D. H., Pace, C. J., Nagy, G., Turner, J. N., & Roysam, B. (2002). Rapid automated three-dimensional tracing of neurons from confocal image stacks. Information Technology in Biomedicine, IEEE Transactions on, 6(2), 171–187.CrossRefGoogle Scholar
  3. Andersen, A., & Kak, A. (1984). Simultaneous algebraic reconstruction technique (SART): a superior implementation of the ART algorithm. Ultrasonic Imaging, 6(1), 81–94.CrossRefPubMedGoogle Scholar
  4. Choromanska, A., Chang, S.-F., & Yuste, R. (2012). Automatic reconstruction of neural morphologies with multi-scale tracking. Frontiers in neural circuits, 6.Google Scholar
  5. Cohen, A., Roysam, B., & Turner, J. (1994). Automated tracing and volume measurements of neurons from 3‐D confocal fluorescence microscopy data. Journal of Microscopy, 173(2), 103–114.CrossRefPubMedGoogle Scholar
  6. DeFelipe J, López-Cruz PL, Benavides-Piccione R, Bielza C, Larrañaga P, Anderson S, Burkhalter A, Cauli B, Fairén A, Feldmeyer D (2013) New insights into the classification and nomenclature of cortical GABAergic interneurons. Nature Reviews NeuroscienceGoogle Scholar
  7. Donohue, D. E., & Ascoli, G. A. (2011). Automated reconstruction of neuronal morphology: an overview. Brain Research Reviews, 67(1), 94–102.CrossRefPubMedCentralPubMedGoogle Scholar
  8. Gerig, G., Kubler, O., Kikinis, R., & Jolesz, F. A. (1992). Nonlinear anisotropic filtering of MRI data. Medical Imaging, IEEE Transactions on, 11(2), 221–232.CrossRefGoogle Scholar
  9. Gillette, T. A., Brown, K. M., Svoboda, K., Liu, Y., & Ascoli, G. A. (2011). DIADEMchallenge. Org: a compendium of resources fostering the continuous development of automated neuronal reconstruction. Neuroinformatics, 9(2), 303–304.Google Scholar
  10. Gonzalez-Bellido, P. T., Peng, H., Yang, J., Georgopoulos, A. P., & Olberg, R. M. (2013). Eight pairs of descending visual neurons in the dragonfly give wing motor centers accurate population vector of prey direction. Proceedings of the National Academy of Sciences, 110(2), 696–701.CrossRefGoogle Scholar
  11. Greenspan, H., Anderson, C. H., & Akber, S. (2000). Image enhancement by nonlinear extrapolation in frequency space. Image Processing, IEEE Transactions on, 9(6), 1035–1048.CrossRefGoogle Scholar
  12. Hayman, M., Smith, K., Cameron, N., & Przyborski, S. (2004). Enhanced neurite outgrowth by human neurons grown on solid three-dimensional scaffolds. Biochemical and Biophysical Research Communications, 314(2), 483–488.CrossRefPubMedGoogle Scholar
  13. Helmstaedter, M., Briggman, K. L., Turaga, S. C., Jain, V., Seung, H. S., & Denk, W. (2013). Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature, 500(7461), 168–174.CrossRefPubMedGoogle Scholar
  14. Kawaguchi, Y., Karube, F., & Kubota, Y. (2006). Dendritic branch typing and spine expression patterns in cortical nonpyramidal cells. Cerebral Cortex, 16(5), 696–711.CrossRefPubMedGoogle Scholar
  15. Kim, J. S., Greene, M. J., Zlateski, A., Lee, K., Richardson, M., Turaga, S. C., Purcaro, M., Balkam, M., Robinson, A., & Behabadi, B. F. (2014). Space-time wiring specificity supports direction selectivity in the retina. Nature, 509(7500), 331–336.CrossRefPubMedCentralPubMedGoogle Scholar
  16. Krahe, T. E., El-Danaf, R. N., Dilger, E. K., Henderson, S. C., & Guido, W. (2011). Morphologically distinct classes of relay cells exhibit regional preferences in the dorsal lateral geniculate nucleus of the mouse. The Journal of Neuroscience, 31(48), 17437–17448.CrossRefPubMedGoogle Scholar
  17. Li, Q., Sone, S., & Doi, K. (2003). Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans. Medical Physics, 30, 2040.CrossRefPubMedGoogle Scholar
  18. Lu, J., Fiala, J. C., & Lichtman, J. W. (2009). Semi-automated reconstruction of neural processes from large numbers of fluorescence images. PloS One, 4(5), e5655.CrossRefPubMedCentralPubMedGoogle Scholar
  19. Oberlaender, M., Bruno, R. M., Sakmann, B., & Broser, P. J. (2007). Transmitted light brightfield mosaic microscopy for three-dimensional tracing of single neuron morphology. Journal of Biomedical Optics, 12(6), 064029.Google Scholar
  20. Peng, H., Ruan, Z., Atasoy, D., & Sternson, S. (2010a). Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model. Bioinformatics, 26(12), i38–i46.CrossRefPubMedCentralPubMedGoogle Scholar
  21. Peng, H., Ruan, Z., Long, F., Simpson, J. H., & Myers, E. W. (2010b). V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnology, 28(4), 348–353.CrossRefPubMedCentralPubMedGoogle Scholar
  22. Peng, H., Long, F., & Myers, G. (2011). Automatic 3D neuron tracing using all-path pruning. Bioinformatics, 27(13), i239–i247.CrossRefPubMedCentralPubMedGoogle Scholar
  23. Peng, H., Roysam, B., & Ascoli, G. A. (2013). Automated image computing reshapes computational neuroscience. BMC Bioinformatics, 14(1), 293.CrossRefPubMedCentralPubMedGoogle Scholar
  24. Peng, H., Bria, A., Zhou, Z., Iannello, G., & Long, F. (2014). Extensible visualization and analysis for multidimensional images using Vaa3D. Nature Protocols, 9(1), 193–208.CrossRefPubMedGoogle Scholar
  25. Rutovitz D (1968) Data structures for operations on digital images. Pictorial pattern recognition:105–133Google Scholar
  26. Sato Y, Nakajima S, Atsumi H, Koller T, Gerig G, Yoshida S, Kikinis R 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. In: CVRMed-MRCAS’97, 1997. Springer, pp 213–222Google Scholar
  27. The Brain Vasculature (BraVa) database (2014). The Krasnow Institute for Advanced Study, George Mason University.
  28. Weickert J (1996) Theoretical foundations of anisotropic diffusion in image processing. In: Theoretical foundations of computer vision. Springer, pp 221–236Google Scholar
  29. Wright SN, Kochunov P, Mut F, Bergamino M, Brown KM, Mazziotta JC, Toga AW, Cebral JR, Ascoli GA (2013) Digital Reconstruction and Morphometric Analysis of Human Brain Arterial Vasculature from Magnetic Resonance Angiography. NeuroImageGoogle Scholar
  30. Xiao, H., & Peng, H. (2013). APP2: automatic tracing of 3D neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree. Bioinformatics, 29(11), 1448–1454.CrossRefPubMedCentralPubMedGoogle Scholar
  31. Yu, Y., & Acton, S. T. (2002). Speckle reducing anisotropic diffusion. Image Processing, IEEE Transactions on, 11(11), 1260–1270.CrossRefGoogle Scholar
  32. Zhao, T., Xie, J., Amat, F., Clack, N., Ahammad, P., Peng, H., Long, F., & Myers, E. (2011). Automated reconstruction of neuronal morphology based on local geometrical and global structural models. Neuroinformatics, 9(2–3), 247–261.CrossRefPubMedCentralPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Zhi Zhou
    • 1
  • Staci Sorensen
    • 1
  • Hongkui Zeng
    • 1
  • Michael Hawrylycz
    • 1
  • Hanchuan Peng
    • 1
  1. 1.Allen Institute for Brain ScienceSeattleUSA

Personalised recommendations