Automated High-Resolution Structure Analysis of Plant Root with a Morphological Image Filtering Algorithm

Liang Gong,Chengliang Liu,Kai Zhu,Wanqi Liang,Xingqiang Du ,Chen-Hui Jerry Lin

doi:10.1155/2021/4021426

Liang Gong, Chengliang Liu + Show 4 more

Open Access

https://doi.org/10.1155/2021/4021426

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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.

Highlights

In the field of biology and genetic breeding, plant phenotypic refers to the external traits of organisms determined by the genotype and environment of the plant, such as shape, structure, size, and color
To ensure that the main roots or lateral roots of different thicknesses and transparency can be segmented as accurately as possible, we introduce an improved Frangi filter algorithm to enhance the image and use multiple Otsu threshold segmentation
We have shown that the improved Frangi filtering algorithm (IFFA) method can achieve outstanding performance in the enhancement of images of rice roots that grow in transparent plastic bags

Summary

Introduction

In the field of biology and genetic breeding, plant phenotypic refers to the external traits of organisms determined by the genotype and environment of the plant, such as shape, structure, size, and color. Root system architecture shows great importance in studying interspecific interactions and genetic improvement of crops [1, 2]. In Gernot Bodner’s research, a data processing pipeline was developed for automatic root segmentation [3]. [8] extracts the main features using a morphological filter algorithm to reconstruct fine roots with high fidelity. In addition to 2D image processing and X-ray microtomography [12], Fang et al focus on 3D reconstruction and dynamic modeling [13]. Compared to the former methods, a cost-effective and efficient way to automatically extract precise root system architecture from high-resolution root images is proposed in this article. Since rice is the main food source, this article would take rice roots as a research object

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