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2013 IEEE Conference on Computer Vision and Pattern Recognition (2010)
San Francisco, CA, USA
June 13, 2010 to June 18, 2010
ISBN: 978-1-4244-6984-0
pp: 2902-2909
Joachim M. Buhmann , Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
Verena Kaynig , Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
Thomas Fuchs , Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
ABSTRACT
In the field of neuroanatomy, automatic segmentation of electron microscopy images is becoming one of the main limiting factors in getting new insights into the functional structure of the brain. We propose a novel framework for the segmentation of thin elongated structures like membranes in a neuroanatomy setting. The probability output of a random forest classifier is used in a regular cost function, which enforces gap completion via perceptual grouping constraints. The global solution is efficiently found by graph cut optimization. We demonstrate substantial qualitative and quantitative improvement over state-of the art segmentations on two considerably different stacks of ssTEM images as well as in segmentations of streets in satellite imagery. We demonstrate that the superior performance of our method yields fully automatic 3D reconstructions of dendrites from ssTEM data.
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CITATION
Joachim M. Buhmann, Verena Kaynig, Thomas Fuchs, "Neuron geometry extraction by perceptual grouping in ssTEM images", 2013 IEEE Conference on Computer Vision and Pattern Recognition, vol. 00, no. , pp. 2902-2909, 2010, doi:10.1109/CVPR.2010.5540029
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