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. 2019 Jun 13;20:326. doi: 10.1186/s12859-019-2926-y

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© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 16 — Automated particle picking results on the two datasets. a A cryo-EM image with a high identical particle density and a lack low-frequency from the Apoferritin dataset. b A low SNR cryo-EM image from the Apoferritin dataset. c A micrograph image from the KLH dataset that includes excessively overlapped particles due to confounding artifacts such as ice contamination, degraded particles, and particle aggregates. d A micrograph image from the KLH dataset that has a very low spatial density and different intensity levels. e and (f) Particle picking results using Intensity Based Clustering Algorithm (ICB) (Apoferritin dataset). i and (j) Particle picking results using k-means (Apoferritin dataset). m and (n) Particle picking results using FCM (Apoferritin dataset). g and (h) Particle picking results using Intensity Based Clustering Algorithm (ICB) (KLH dataset). k and (l) Particle picking results using k-means (KLH dataset). o and (p) Particle picking results using FCM (KLH dataset)