Abstract
The accuracy and reliability of automated neurite tracing systems is ultimately limited by image quality as reflected in the signal-to-noise ratio, contrast, and image variability. This paper describes a novel combination of image processing methods that operate on images of neurites captured by confocal and widefield microscopy, and produce synthetic images that are better suited to automated tracing. The algorithms are based on the curvelet transform (for denoising curvilinear structures and local orientation estimation), perceptual grouping by scalar voting (for elimination of non-tubular structures and improvement of neurite continuity while preserving branch points), adaptive focus detection, and depth estimation (for handling widefield images without deconvolution). The proposed methods are fast, and capable of handling large images. Their ability to handle images of unlimited size derives from automated tiling of large images along the lateral dimension, and processing of 3-D images one optical slice at a time. Their speed derives in part from the fact that the core computations are formulated in terms of the Fast Fourier Transform (FFT), and in part from parallel computation on multi-core computers. The methods are simple to apply to new images since they require very few adjustable parameters, all of which are intuitive. Examples of pre-processing DIADEM Challenge images are used to illustrate improved automated tracing resulting from our pre-processing methods.
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Acknowledgements
The authors wish to thank Dr. Marco Reisert (Albert-Ludwigs-Universität Freiburg) for taking his time to share his 3D spherical harmonic tensor voting code (Reisert and Burkhardt 2008) with us and helping us use it. His suggestions on ways to apply 3-D tensor voting to our data have been invaluable. This work was supported primarily by NIH grant R01 EB005157 and by NSF grant EEC-9986821. We would also like to thank the DIADEM organizers and the dataset providers for sharing their data. It really helped us to test our algorithms on a variety of image modalities and compare it with other finalists.
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Narayanaswamy, A., Wang, Y. & Roysam, B. 3-D Image Pre-processing Algorithms for Improved Automated Tracing of Neuronal Arbors. Neuroinform 9, 219–231 (2011). https://doi.org/10.1007/s12021-011-9116-z
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DOI: https://doi.org/10.1007/s12021-011-9116-z