High-Throughput, Algorithmic Determination of Nanoparticle Structure from Electron Microscopy Images

Automated analysis of electron microscopy images (PC and Mac versions available.)

For full details of use see article and supplementary information found here: http://pubs.acs.org/doi/full/10.1021/acsnano.5b05968
BeanCounter.m is the main script responsible for analyzing EM images. Users can pass images into this script through a graphical user interface (GUI) and the script will output a single excel sheet containing particle statistics. If the user selects an entire directory for analysis, BeanCounter.m will aggregate each individual excel sheet into a single excel document. Users can specify the output location of these excel files using the GUI. The excel sheet contains: minor edge length, minor edge length error, aspect ratio, aspect ratio error, corner rounding, corner rounding error, goodness of fit, area, perimeter, and the shape classification or identifier. All errors are determined by the confidence interval of the fit. The shape identifier is a number that corresponds to a particular shape (i.e. 1: rod, 2: circle, 3: triangle, 4: square, 6: hexagon). The GUI has additional options for specifying output images and processing parameters.
Copyright (c) 2015, Chad A. Mirkin, Christine R. Laramy, Keith A. Brown, and Matthew O'Brien.
All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
* Scientific works derived from this code must include a citation to the paper where it was introduced:
M. N. O'Brien, M. R. Jones, K. A. Brown, C. A. Mirkin. Universal Noble Metal Nanoparticle Seeds Realized Through Iterative Reductive Growth and Oxidative Dissolution Reactions. J. Am. Chem. Soc. 2014, 136, 7603 doi: 10.1021/ja503509k
High-Throughput, Algorithmic Determination of Nanoparticle Structure from Electron Microscopy Images
Christine R. Laramy, Keith A. Brown, Matthew N. O’Brien, and Chad. A. Mirkin ACS Nano Article ASAP
DOI: 10.1021/acsnano.5b05968.
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