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Spatially regularized multiscale graph clustering for electron microscopy
Author(s): Nathan Kapsin; James M. Murphy
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Paper Abstract

We propose an unsupervised, multiscale learning method for the segmentation of electron microscopy (EM) imagery. Large EM images are first coarsely clustered using spectral graph analysis, thereby non-locally and non-linearly denoising the data. The resulting coarse-scale clusters are then considered as vertices of a new graph, which is analyzed to derive a clustering of the original image. The two-stage approach is multiscale and enjoys robustness to noise and outlier pixels. A quasilinear and parallelizable implementation is presented, allowing the proposed method to scale to images with billions of pixels. Strong empirical performance is observed compared to conventional unsupervised techniques.

Paper Details

Date Published: 14 May 2019
PDF: 8 pages
Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109860S (14 May 2019); doi: 10.1117/12.2519140
Show Author Affiliations
Nathan Kapsin, The Univ. of Chicago (United States)
James M. Murphy, Tufts Univ. (United States)


Published in SPIE Proceedings Vol. 10986:
Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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