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. 2019 Mar 14;9:4501. doi: 10.1038/s41598-018-37748-4

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© The Author(s) 2019

Open Access This 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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Example of the radiomics feature extraction. (a) Each tumor was first manually segmented on a representative US image (left) and subsequently, the position information of the ROI (middle) was collected and applied to the US image without marking the ROI itself, allowing the ROI to be extracted from the original US image (right). (b) Intensity histogram of the ROI image is shown. First and second order statistics values were calculated for each image. (c) For further feature extraction, the wavelet transform was used. For clearer presentation, the wavelet coefficients were scaled into a range from 0 to 255. From left to right: wavelet decompositions of the original image using LL, LH, HL, and HH, where L and H are low- and high-pass filters in the x- and y-directions, respectively.