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 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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Automated high-resolution structure analysis of plant root with a morphological image filtering algorithm

10.21203/rs.3.rs-20061/v1 ◽

2020 ◽

Author(s):

Liang Gong ◽

Chenhui Lin ◽

Kai Zhu ◽

Chenliang Liu ◽

Jun Hong ◽

...

Keyword(s):

Oryza Sativa ◽

Lateral Roots ◽

Gaussian Kernel ◽

Connected Components ◽

Biological Research ◽

Dice Similarity Coefficient ◽

Pass Filter ◽

Low Pass Filter ◽

Filtering Algorithm ◽

Primary Roots

Abstract Background: Research on rice (Oryza sativa) roots requires the automatic analysis of root structure using image processing. It is challenging for a digital filter to identify the roots from the obscured and cluttered background, and to separate the primary roots from lateral roots. The original Frangi filter (FF), presented by Alejandro F. Frangi in 1998, is a low-pass filter dedicated to blood vessel image enhancement. Considering the similarity between vessels and roots, the FF is applied to identify the roots. However, the original FF only enhances the tube-like primary roots but erases the lateral roots. Hence, a new method is developed to meet the demands by simultaneously maintaining the primary and lateral morphological structure of roots. Result: In this work, a crucial part of the FF, Gaussian filtering, is redesigned to discriminate against the primary and lateral roots in a 2-D image. Inspired by the structure-awareness of the FF, an Improved Frangi Filtering Algorithm (IFFA) designed for plant roots is proposed. First, Multilevel image thresholding, connected-components labeling and width correction are used to optimize the resultant binary image. Then, to enhance the local structure, the truncated Gaussian kernel is modified resulting in more discernible lateral roots. Compared to the original FF and the Automatic Root Image Analysis (ARIA), a commercial software, IFFA is a faster and more accurate algorithm achieving an identification accuracy of 97.48%. Conclusion: IFFA is an effective 2-D filtering approach to enhance the roots of rice (Oryza sativa) for segmentation and further biological research. IFFA is faster than ARIA and the original FF, and IFFA’s accuracy outperforms its counterparts as per the Intersection Over Union (IOU) and Dice Similarity Coefficient (DSC) criteria.

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Memristor bridge-based low pass filter for image processing

Journal of Systems Engineering and Electronics ◽

10.21629/jsee.2019.03.02 ◽

2019 ◽

Vol 30 (03) ◽

pp. 448-455 ◽

Cited By ~ 2

Author(s):

Yongbin Yu ◽

◽

Nijing Yang ◽

Chenyu Yang ◽

Tashi Nyima ◽

...

Keyword(s):

Image Processing ◽

Pass Filter ◽

Low Pass Filter ◽

Low Pass

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Design of Fast Second-Order IIR Low-Pass Filter Based on Parameter Optimization

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.4382 ◽

2014 ◽

Vol 644-650 ◽

pp. 4382-4386 ◽

Cited By ~ 1

Author(s):

Qi Rui Li ◽

Ming Ming Jiang ◽

Bing Luo ◽

Xiao Ping Hu ◽

Kang Hua Tang

Keyword(s):

Operation Time ◽

Digital Signal ◽

Second Order ◽

Filter Coefficient ◽

Matlab Simulation ◽

Pass Filter ◽

Low Pass Filter ◽

Filtering Algorithm ◽

Low Pass ◽

Fast Filter

Second-order IIR low-pass filter is widely used in digital signal processing. A fast filter algorithm without multiplication is proposed for it in this paper. A configuration method for the filter coefficient of fast algorithm is put forward, considering the characteristic of second-order IIR low-pass filter coefficient and the filter’s mathematical model. A performance analysis of fast second-order IIR low-pass filter without multiplication is shown in the paper, and the performance of the fast filter is shown by example using MATLAB simulation. What’s more, the algorithm’s rapidity is verified by an implementation of the filter on FPGA, which turns out that fast filtering algorithm takes only 54.7% of common algorithm’s operation time to realize the same filer function.

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Enhancing SNR and generating contrast for cryo-EM images with convolutional neural networks

10.1101/2020.08.16.253070 ◽

2020 ◽

Author(s):

Eugene Palovcak ◽

Daniel Asarnow ◽

Melody G. Campbell ◽

Zanlin Yu ◽

Yifan Cheng

Keyword(s):

Image Processing ◽

High Frequency ◽

Signal To Noise Ratio ◽

Three Dimensional ◽

Low Frequency ◽

Pass Filter ◽

Low Pass Filter ◽

Processing Pipeline ◽

Visual Contrast ◽

Low Pass

AbstractIn cryogenic electron microscopy (cryo-EM) of radiation-sensitive biological samples, both the signal-to-noise ratio (SNR) and the contrast of images are critically important in the image processing pipeline. Classic methods improve low-frequency image contrast experimentally, by imaging with high defocus, or computationally, by applying various types of low-pass filter. These contrast improvements typically come at the expense of high-frequency SNR, which is suppressed by high-defocus imaging and removed by low pass filtration. Here, we demonstrate that a convolutional neural network (CNN) denoising algorithm can be used to significantly enhance SNR and generate contrast in cryo-EM images. We provide a quantitative evaluation of bias introduced by the denoising procedure and its influences on image processing and three-dimensional reconstructions. Our study suggests that besides enhancing the visual contrast of cryo-EM images, the enhanced SNR of denoised images may facilitate better outcomes in the other parts of the image processing pipeline, such as classification and 3D alignment. Overall, our results provide a ground of using denoising CNNs in the cryo-EM image processing pipeline.

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Reproducibility of Immunostaining Quantification and Description of a New Digital Image Processing Procedure for Quantitative Evaluation of Immunohistochemistry in Pathology

Microscopy and Microanalysis ◽

10.1017/s1431927609090710 ◽

2009 ◽

Vol 15 (4) ◽

pp. 353-365 ◽

Cited By ~ 19

Author(s):

Vagner Bernardo ◽

Simone Q.C. Lourenço ◽

Renato Cruz ◽

Luiz H. Monteiro-Leal ◽

Licínio E. Silva ◽

...

Keyword(s):

Image Processing ◽

Quantitative Evaluation ◽

Digital Image ◽

Visual Observation ◽

Human Vision ◽

Intraobserver Variability ◽

Image Subtraction ◽

Specific Staining ◽

Pass Filter ◽

Low Pass Filter

AbstractQuantification of immunostaining is a widely used technique in pathology. Nonetheless, techniques that rely on human vision are prone to inter- and intraobserver variability, and they are tedious and time consuming. Digital image analysis (DIA), now available in a variety of platforms, improves quantification performance: however, the stability of these different DIA systems is largely unknown. Here, we describe a method to measure the reproducibility of DIA systems. In addition, we describe a new image-processing strategy for quantitative evaluation of immunostained tissue sections using DAB/hematoxylin-stained slides. This approach is based on image subtraction, using a blue low pass filter in the optical train, followed by digital contrast and brightness enhancement. Results showed that our DIA system yields stable counts, and that this method can be used to evaluate the performance of DIA systems. The new image-processing approach creates an image that aids both human visual observation and DIA systems in assessing immunostained slides, delivers a quantitative performance similar to that of bright field imaging, gives thresholds with smaller ranges, and allows the segmentation of strongly immunostained areas, all resulting in a higher probability of representing specific staining. We believe that our approach offers important advantages to immunostaining quantification in pathology.

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Window Memoization In Software As Applied To Image Processing Algorithms

10.32920/ryerson.14654973.v1 ◽

2021 ◽

Author(s):

Tahir Jaffer

Keyword(s):

Image Processing ◽

Lower Bound ◽

Detection Algorithm ◽

Corner Detection ◽

Image Processing Algorithm ◽

Pass Filter ◽

Pixel Intensity ◽

Low Pass Filter ◽

Low Pass ◽

Speed Up

A new local image processing algorithm, the Tahir algorithm, is an adaptation to the standard low-pass filter. Its design is for images that have the spectrum of pixel intensity concentrated at the lower end of the intensity spectrum. Window memoization is a specialization of memoization. Memoization is a technique to reduce computational redundancy by skipping redundant calculations and storing results in memory. An adaptation for window memozation is developed based on improved symbol generation and a new eviction policy. On implementation, the mean lower-bound speed-up achieved was between 0.32 (slowdown of approximately 3) and 3.70 with a peak of 4.86. Lower-bound speed-up is established by accounting for the time to create and delete the cache. Window memoization was applied to: the convolution technique, Trajkovic corner detection algorithm and the Tahir algorithm. Window memoization can be evaluated by calculating both the speed-up achieved and the error introduced to the output image.

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