Topological Image Analysis and (Normalised) Representations for Plant Phenotyping

Janusch, Ines; Kropatsch, Walter G.; Busch, Wolfgang

doi:10.1109/synasc.2014.83

Cited by 2 publications

(2 citation statements)

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“…Graph-based methods have been used in analyzing X-ray CT images for segmentation and quantitative analysis of porous materials [68] and airway wall structure [69]. Graph-based methods have also been used for root-phenotyping based on 2D images [70,71] and likely extend to root phenotyping based on 3D X-ray images.…”

Section: Graph-based Segmentationmentioning

confidence: 99%

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

Valdes

Clarke

2018

Agronomy

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Abstract:Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge. We review statistical and computational approaches that have been or may be effective for the analysis of 3D CT images of plant roots. We describe and comment on different approaches to aspects of the analysis of plant roots based on images, namely, (1) root segmentation, i.e., the isolation of root from non-root matter; (2) root-system reconstruction; and (3) extraction of higher-level phenotypes. As many of these approaches are novel and have yet to be applied to this context, we limit ourselves to brief descriptions of the methodologies. With the rapid development and growing use of X-ray CT scanning technologies to generate large volumes of data relevant to root structure, it is timely to review existing and potential quantitative and computational approaches to the analysis of such data. Summaries of several computational tools are included in the Appendix.

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Section: Graph-based Segmentationmentioning

confidence: 99%

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

Valdes

Clarke

2018

Agronomy

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“…The segmented image may then be analyzed to extract root 58 phenes. For example, Janusch et al (2014) proposed to identify root topology using Reeb graphs, 59 which depicts the topological structure of the root shape as the connectivity of level sets. Chen et al 60 (2006) described a method to determine the branching structure in wheat using Markov chains, where 61 the lateral root branching probability was found using the locations of the lateral roots along the main 62 root.…”

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confidence: 99%

A novel multi-perspective imaging platform (M-PIP) for phenotyping soybean root crowns in the field increases throughput and separation ability of genotype root properties

Seethepalli

York

Almtarfi

et al. 2018

Preprint

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A b s t r a c t 15 Background: Root crown phenotyping has linked root properties to shoot mass, nutrient uptake, and 16 yield in the field, which increases the understanding of soil resource acquisition and presents 17 opportunities for breeding. The original methods using manual measurements have been largely 18 supplanted by image-based approaches. However, most image-based systems have been limited to one 19 or two perspectives and rely on segmentation from grayscale images. An efficient high-throughput root 20 crown phenotyping system is introduced that takes images from five perspectives simultaneously, 21 constituting the Multi-Perspective Imaging Platform (M-PIP). A segmentation procedure using the 22 Expectation-Maximization Gaussian Mixture Model (EM-GMM) algorithm was developed to 23 distinguish plant root pixels from background pixels in color images and using hardware acceleration 24 (CPU and GPU). Phenes were extracted using MatLab scripts. Placement of excavated root crowns for 25 image acquisition was standardized and is ergonomic. The M-PIP was tested on 24 soybean [Glycine 26 max (L.) Merr.] cultivars released between 1930 and 2005 . 27 Results: Relative to previous reports of imaging throughput, this system provides greater throughput 28 with sustained rates of 1.66 root crowns min -1 . The EM-GMM segmentation algorithm with hardware 29 acceleration was able to segment images in 10 s, faster than previous methods, and the output images 30 were consistently better connected with less loss of fine detail. Image-based phenes had similar 31 heritabilities as manual measures with the greatest effect sizes observed for Maximum Radius and Fine 32 Radius Frequency. Correlations were also noted, especially among the manual Complexity score and 33 phenes such as number of roots and Total Root Length. Averaging phenes across perspectives 34 3 generally increased heritability, and no single perspective consistently performed better than others. 35Angle-based phenes, Fineness Index, Maximum Width, Holes, Solidity and Width-to-Depth Ratio were 36 the most sensitive to perspective with decreased correlations among perspectives. 37Conclusion: The substantial heritabilities measured for many phenes suggest that they are potentially 38 useful for breeding. Multiple perspectives together often produced the greatest heritabilities, and no 39 single perspective consistently performed better than others. Thus, as illustrated here for soybean, 40 multiple perspectives may be beneficial for root crown phenotyping systems. This system can 41 contribute to breeding efforts that incorporate under-utilized root phenotypes to increase food security 42 and sustainability. 43

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Topological Image Analysis and (Normalised) Representations for Plant Phenotyping

Cited by 2 publications

References 13 publications

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

A novel multi-perspective imaging platform (M-PIP) for phenotyping soybean root crowns in the field increases throughput and separation ability of genotype root properties

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