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. 2021 Oct 22;21:1133. doi: 10.1186/s12885-021-08542-9

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© The Author(s) 2021

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

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Fig. 8 — Quantitative features presented in the 3D prostate volume demonstrate variation of signal intensities between features and in different anatomical parts of the prostate. Approximately 20% of highest ranking features were plotted with bone colormap, where lighter colors represent higher values. Amount of nuclei is highest near urethra, representing high density of cells in ducts and urethral muscle layer compared to sparse prostate glandular structures distal to the urethra (a). Energy (sum of squared elements) in grayscale co-occurrence matrix is strongest at the top and bottom of the prostate (b). Histogram of oriented gradients features with 9 pixel window are spread around the prostate (c). Local binary patterns tend to have strong signals around lateral and ventral prostate (d)