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RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues.

Chopin J, Laga H, Huang CY, Heuer S, Miklavcic SJ - PLoS ONE (2015)

Bottom Line: The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake.We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data.The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.

View Article: PubMed Central - PubMed

Affiliation: Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, South Australia, Australia.

ABSTRACT
The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.

No MeSH data available.


Challenging images make local thresholding a necessity.(a): Input images with regions of varying intensity. (b): Segmentation result using RootScan’s global thresholding method. (c): Segmentation result using RootAnalyzer’s local thresholding method.
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pone.0137655.g004: Challenging images make local thresholding a necessity.(a): Input images with regions of varying intensity. (b): Segmentation result using RootScan’s global thresholding method. (c): Segmentation result using RootAnalyzer’s local thresholding method.

Mentions: In many of the root cross section images we analyzed for this paper, the backgrounds were largely inhomogeneous. Thus, global segmentation often fails. In contrast, the local thresholding method described above was capable of accurately segmenting all images. This is exemplified in Fig 4 where it is demonstrated that large areas of varying intensity, such as the large dark region in the top image of Fig 4(a) and the large blue region in the bottom image of Fig 4a, are correctly dealt with. One artefact of the local thresholding technique, however, is that some small foreground connected components, or noise, are regularly detected. We point out in the Classifying Cells subsection that the area-based cell classification automatically filters out this segmentation noise.


RootAnalyzer: A Cross-Section Image Analysis Tool for Automated Characterization of Root Cells and Tissues.

Chopin J, Laga H, Huang CY, Heuer S, Miklavcic SJ - PLoS ONE (2015)

Challenging images make local thresholding a necessity.(a): Input images with regions of varying intensity. (b): Segmentation result using RootScan’s global thresholding method. (c): Segmentation result using RootAnalyzer’s local thresholding method.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4580584&req=5

pone.0137655.g004: Challenging images make local thresholding a necessity.(a): Input images with regions of varying intensity. (b): Segmentation result using RootScan’s global thresholding method. (c): Segmentation result using RootAnalyzer’s local thresholding method.
Mentions: In many of the root cross section images we analyzed for this paper, the backgrounds were largely inhomogeneous. Thus, global segmentation often fails. In contrast, the local thresholding method described above was capable of accurately segmenting all images. This is exemplified in Fig 4 where it is demonstrated that large areas of varying intensity, such as the large dark region in the top image of Fig 4(a) and the large blue region in the bottom image of Fig 4a, are correctly dealt with. One artefact of the local thresholding technique, however, is that some small foreground connected components, or noise, are regularly detected. We point out in the Classifying Cells subsection that the area-based cell classification automatically filters out this segmentation noise.

Bottom Line: The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake.We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data.The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.

View Article: PubMed Central - PubMed

Affiliation: Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, South Australia, Australia.

ABSTRACT
The morphology of plant root anatomical features is a key factor in effective water and nutrient uptake. Existing techniques for phenotyping root anatomical traits are often based on manual or semi-automatic segmentation and annotation of microscopic images of root cross sections. In this article, we propose a fully automated tool, hereinafter referred to as RootAnalyzer, for efficiently extracting and analyzing anatomical traits from root-cross section images. Using a range of image processing techniques such as local thresholding and nearest neighbor identification, RootAnalyzer segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and epidermis, and subsequently produces statistics about the morphological properties of the root cells and tissues. We use RootAnalyzer to analyze 15 images of wheat plants and one maize plant image and evaluate its performance against manually-obtained ground truth data. The comparison shows that RootAnalyzer can fully characterize most root tissue regions with over 90% accuracy.

No MeSH data available.