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. 2018 Mar 2;8:3916. doi: 10.1038/s41598-018-22251-7

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© The Author(s) 2018

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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Statistical distribution of features used to describe retinal tufts. (A) Flat mounted mouse retina stained with lectin (red), where the consensual tuft areas are enhanced in magenta. (B) Zoom in the square area in (A) depicting the subdivision in small squares (1% of Φ_retina). (C) Violin plots illustrating the distribution of several features computed within the small square regions depicted in (B) I_loc, I_g, and I_LoG are normalized quantities, and LBP₀ and LBP₉ are the fraction of pixels with code 0 and 9 within each region, respectively. We only display features presenting a significant difference between tuft and normal regions. All features are expressed in arbitrary units, and for each one, the similarity between distributions is expressed as the absolute difference between medians normalized by the standard deviation of the values of normal vessels. All of them differ more than one standard deviation.