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2015 IEEE Winter Conference on Applications of Computer Vision (WACV) (2015)
Waikoloa, HI, USA
Jan. 5, 2015 to Jan. 9, 2015
ISBN: 978-1-4799-6683-7
pp: 915-920
We present a novel method for automated segmentation of axons in extremely noisy videos obtained via two-photon microscopy in awake mice. We formulate segmentation as a pixel-wise classification problem in which a pixel is classified into "axon" or "non-axon" based on its feature vector. In order to deal with high levels of noise, the features of our classifier are derived from spatio-temporal Independent Component Analysis (stICA) which effectively isolates noise from signal components while leveraging temporal coherence from the video. We fit parametric models to represent the distribution of the extracted features and apply a probabilistic classifier over stICA components to determine the label of each pixel. Finally, we show compelling qualitative and quantitative results from very challenging two-photon microscopic, demonstrating the usefulness of our approach. An example time-series of two-photon images with our automated ROI extraction over layed is available with the supplemental materials.
Nerve fibers, Image segmentation, Feature extraction, Principal component analysis, Microscopy, Videos, Noise measurement

J. Bowler, R. Feris, L. Cao, J. Wang and M. Zhou, "Automated Axon Segmentation from Highly Noisy Microscopic Videos," 2015 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA, 2015, pp. 915-920.
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