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. 2020 Sep 23;10:15532. doi: 10.1038/s41598-020-72433-5

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Non-invasive thermal image processing. (A) Non-invasive thermal images (IRON scale) of the mice were captured weekly. (B, C) Thermal images were processed by our algorithm which extracts multiple features from the selected region of interest (ROI) covering the liver. Displayed are the ROI (B) and the Graphical User Interface (C) we developed. (D) Output parameters were fed as input into a t-SNE dimensionality reduction algorithm, followed by k-means clustering. t-SNE t-distributed stochastic neighbor embedding.