Adaptive Image Enhancement for Tracing 3D Morphologies of Neurons and Brain Vasculatures
- 811 Downloads
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.
KeywordsAdaptive 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.
- Choromanska, A., Chang, S.-F., & Yuste, R. (2012). Automatic reconstruction of neural morphologies with multi-scale tracking. Frontiers in neural circuits, 6.Google Scholar
- 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
- 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
- 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
- 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
- Rutovitz D (1968) Data structures for operations on digital images. Pictorial pattern recognition:105–133Google Scholar
- 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
- The Brain Vasculature (BraVa) database (2014). The Krasnow Institute for Advanced Study, George Mason University. http://cng.gmu.edu/brava.
- Weickert J (1996) Theoretical foundations of anisotropic diffusion in image processing. In: Theoretical foundations of computer vision. Springer, pp 221–236Google Scholar
- 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