Automated High-Resolution Structure Analysis of Plant Root with a Morphological Image Filtering Algorithm
Research on rice (Oryza sativa) roots demands the automatic analysis of root architecture during image processing. It is challenging for a digital filter to identify the roots from the obscure and cluttered background. The original Frangi algorithm, presented by Alejandro F. Frangi in 1998, is a suc...
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| Published in: | Mathematical problems in engineering Vol. 2021; pp. 1 - 14 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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New York
Hindawi
2021
John Wiley & Sons, Inc |
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| ISSN: | 1024-123X, 1563-5147 |
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| Abstract | Research on rice (Oryza sativa) roots demands the automatic analysis of root architecture during image processing. It is challenging for a digital filter to identify the roots from the obscure and cluttered background. The original Frangi algorithm, presented by Alejandro F. Frangi in 1998, is a successful low-pass filter dedicated to blood vessel image enhancement. Considering the similarity between vessels and roots, the Frangi filter algorithm is applied to outline the roots. However, the original Frangi only enhances the tube-like primary roots but erases the lateral roots during filtering. In this paper, an improved Frangi filtering algorithm (IFFA), designed for plant roots, is proposed. Firstly, an automatic root phenotyping system is designed to fulfill the high-throughput root image acquisition. Secondly, multilevel image thresholding, connected components labeling, and width correction are used to optimize the output binary image. Thirdly, to enhance the local structure, the Gaussian filtering operator in the original Frangi is redesigned with a truncated Gaussian kernel, resulting in more discernible lateral roots. Compared to the original Frangi filter and commercially available software, IFFA is faster and more accurate, achieving a pixel accuracy of 97.48%. IFFA is an effective morphological filtering approach to enhance the roots of rice for segmentation and further biological research. It is convincing that IFFA is suitable for different 2-D plant root image processing and morphological analysis. |
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| AbstractList | Research on rice (Oryza sativa) roots demands the automatic analysis of root architecture during image processing. It is challenging for a digital filter to identify the roots from the obscure and cluttered background. The original Frangi algorithm, presented by Alejandro F. Frangi in 1998, is a successful low-pass filter dedicated to blood vessel image enhancement. Considering the similarity between vessels and roots, the Frangi filter algorithm is applied to outline the roots. However, the original Frangi only enhances the tube-like primary roots but erases the lateral roots during filtering. In this paper, an improved Frangi filtering algorithm (IFFA), designed for plant roots, is proposed. Firstly, an automatic root phenotyping system is designed to fulfill the high-throughput root image acquisition. Secondly, multilevel image thresholding, connected components labeling, and width correction are used to optimize the output binary image. Thirdly, to enhance the local structure, the Gaussian filtering operator in the original Frangi is redesigned with a truncated Gaussian kernel, resulting in more discernible lateral roots. Compared to the original Frangi filter and commercially available software, IFFA is faster and more accurate, achieving a pixel accuracy of 97.48%. IFFA is an effective morphological filtering approach to enhance the roots of rice for segmentation and further biological research. It is convincing that IFFA is suitable for different 2-D plant root image processing and morphological analysis. |
| Author | Gong, Liang Zhu, Kai Liu, Chengliang Du, Xiaofeng Lin, Chenhui Liang, Wanqi |
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| Cites_doi | 10.1371/journal.pone.0108255 10.1023/A:1026179919689 10.1186/s13007-015-0084-4 10.1016/j.geoderma.2006.04.009 10.1111/j.1365-313X.2009.04009.x 10.1016/j.compag.2021.106197 10.1023/B:PLSO.0000030171.28821.55 10.1007/bfb0056195 10.1186/s13007-016-0131-9 10.2134/agronj1997.00021962008900060014x 10.1186/s13007-018-0352-1 10.1007/BF01189228 10.1109/TSMC.1979.4310076 10.1109/3DV.2016.79 10.1104/pp.109.150748 |
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| Copyright | Copyright © 2021 Liang Gong et al. Copyright © 2021 Liang Gong et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| SubjectTerms | Algorithms Automation Blood vessels Cameras Digital filters Digital imaging Eigenvalues Image acquisition Image enhancement Image filters Image processing Image segmentation Low pass filters Mathematical problems Morphology Noise Plant roots Structural analysis |
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| Title | Automated High-Resolution Structure Analysis of Plant Root with a Morphological Image Filtering Algorithm |
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