Spatially regularized multiscale graph clustering for electron microscopy

Nathan Kapsin; James M. Murphy

doi:10.1117/12.2519140

14 May 2019 Spatially regularized multiscale graph clustering for electron microscopy

Nathan Kapsin, James M. Murphy

Author Affiliations +

Proceedings Volume 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV; 109860S (2019) https://doi.org/10.1117/12.2519140
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

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.

Conference Presentation

Citation Download Citation

Nathan Kapsin and James M. Murphy "Spatially regularized multiscale graph clustering for electron microscopy", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109860S (14 May 2019); https://doi.org/10.1117/12.2519140

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE