2020
DOI: 10.21203/rs.3.rs-96550/v1
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An Improved Method for the Segmentation of Roots from X-ray Computed Tomography 3D Images: Rootine v.2

Abstract: BackgroundX-ray computed tomography is acknowledged as a powerful tool for the study of root system architecture of plants growing in soil. In this paper, we improved the original root segmentation algorithm “Rootine” and present its succeeding version “Rootine v.2”. In addition to grey value information, Rootine algorithms are based on shape detection of cylindrical roots. Both algorithms are macros for the ImageJ software and are made freely available to the public. New features in Rootine v.2 are (1) a pot … Show more

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Cited by 3 publications
(11 citation statements)
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“…Root segmentation of each column scan was performed with the algorithm Rootine v2 (Phalempin et al 2021) Rootine v2 is a free macro for the image processing software ImageJ (Schindelin et al 2012). It combines a series of pre-and postprocessing filters with a shape based detection of cylindrical roots at various scales.…”
Section: Root Segmentationmentioning
confidence: 99%
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“…Root segmentation of each column scan was performed with the algorithm Rootine v2 (Phalempin et al 2021) Rootine v2 is a free macro for the image processing software ImageJ (Schindelin et al 2012). It combines a series of pre-and postprocessing filters with a shape based detection of cylindrical roots at various scales.…”
Section: Root Segmentationmentioning
confidence: 99%
“…It should be noted that despite major advances in root segmentation in the past years (Phalempin et al 2021;Soltaninejad et al 2020), we still face the trade-off between image resolution and sample size resulting in fine roots being partly missed out. In the present case, this afflicts the differences between sand and loam as the share of fine roots was larger in loam.…”
Section: System Limitations -Relevance For Field Conditionsmentioning
confidence: 99%
“…Then, a background removal step was applied via an “absolute difference transform” whose rationale and technical aspects are described by Phalempin et al. (2021). The obtained images served as input for the root segmentation per se.…”
Section: Methodsmentioning
confidence: 99%
“…With the new approach, every image was visually analyzed and the diameter of the roots in the image was measured using the “Measure” tool available in ImageJ. The sigma values of the tubeness filter implemented in ImageJ were then calculated based on the measured root diameters according to the formalizing steps described elsewhere (Phalempin et al., 2021). The results of the tubeness filter were segmented using the “3D Hysteresis Thresholding” (Ollion et al., 2013) available in ImageJ.…”
Section: Methodsmentioning
confidence: 99%
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