Fractal Analysis of Root System Architecture by Box-Counting Method and Its Relationship with Zn Accumulation in Rice (<I>Oryza sativa</I> L.)

An essential element of fractal analysis of functional coatings is the fractal dimension, which is an important quantitative characteristic. Typically, coating images are represented as colored or halftone, and most fractal dimension algorithms are for binary images. Therefore, an important step in fractal analysis is binarization, which is a threshold separation operation and the result of which is a binary image.The purpose of the study is to study and program the methods of image binarization and to study the influence of these methods on the value of fractal dimension of functional coatings.As a result of the binarization threshold, the image is split into two regions, one containing all pixels with values below a certain threshold and the other containing all pixels with values above that threshold. Of great importance is the determination of the binarization threshold.The study analyzed a number of functional coating images, determined the fractal dimension of the image by the Box Counting method at different binarization thresholds and when applying different binarization methods (binarization with lower and upper threshold, with double restriction, and the average method for determining the optimal binarization threshold) images. The Box Counting method is used to depict any structure on a plane. This method allows us to determine the fractal dimension of not strictly self-similar objects. Each image binarization method is used for different types of images and for solving different problems.As a result, the methods of image binarization were developed and implemented, the fractal dimension of binary images was calculated, and the influence of these methods on the value of fractal dimension of functional coatings was investigated.The surfaces of composite steel structure, metallic porous materials, and natural cave structures are analyzed.

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A Fractal Analysis of Human Cranial Sutures

The Cleft Palate-Craniofacial Journal ◽

10.1597/1545-1569_2003_040_0409_afaohc_2.0.co_2 ◽

2003 ◽

Vol 40 (4) ◽

pp. 409-415 ◽

Cited By ~ 7

Author(s):

Jack C. Yu ◽

Ronald L. Wright ◽

Matthew A. Williamson ◽

James P. Braselton ◽

Martha L. Abell

Keyword(s):

Fractal Analysis ◽

Cranial Sutures ◽

Box Dimension ◽

Counting Method ◽

Scale Invariant ◽

Box Counting ◽

Invariant Features ◽

Iteration Functions ◽

Self Similar ◽

Box Counting Method

Objectives Many biological structures are products of repeated iteration functions. As such, they demonstrate characteristic, scale-invariant features. Fractal analysis of these features elucidates the mechanism of their formation. The objectives of this project were to determine whether human cranial sutures demonstrate self-similarity and measure their exponents of similarity (fractal dimensions). Design One hundred three documented human skulls from the Terry Collection of the Smithsonian Institution were used. Their sagittal sutures were digitized and the data converted to bitmap images for analysis using box-counting method of fractal software. Results The log-log plots of the number of boxes containing the sutural pattern, Nr, and the size of the boxes, r, were all linear, indicating that human sagittal sutures possess scale-invariant features and thus are fractals. The linear portion of these log-log plots has limits because of the finite resolution used for data acquisition. The mean box dimension, Db, was 1.29289 ± 0.078457 with a 95% confidence interval of 1.27634 to 1.30944. Conclusions Human sagittal sutures are self-similar and have a fractal dimension of 1.29 by the box-counting method. The significance of these findings includes: sutural morphogenesis can be described as a repeated iteration function, and mathematical models can be constructed to produce self-similar curves with such Db. This elucidates the mechanism of actual pattern formation. Whatever the mechanisms at the cellular and molecular levels, human sagittal suture follows the equation log Nr = 1.29 log 1/r, where Nr is the number of square boxes with sides r that are needed to contain the sutural pattern and r equals the length of the sides of the boxes.

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Fractal dimension of external linear contour of human cerebellum (magnetic resonance imaging study)

Reports of Morphology ◽

10.31393/morphology-journal-2021-27(2)-03 ◽

2021 ◽

Vol 27 (2) ◽

pp. 16-22

Author(s):

N.I. Maryenko ◽

O.Y. Stepanenko

Keyword(s):

Fractal Dimension ◽

Magnetic Resonance ◽

Fractal Analysis ◽

Spatial Configuration ◽

Magnetic Resonance Images ◽

Counting Method ◽

Average Value ◽

Box Counting ◽

Human Cerebellum ◽

Box Counting Method

Fractal analysis is a method of mathematical analysis, which provides quantitative assessment of the spatial configuration complexity of the anatomical structures and may be used as a morphometric method. The purpose of the study was to determine the values of the fractal dimension of the outer linear contour of human cerebellum by studying the magnetic resonance images of the brain using the authors’ modification of the caliper method and compare to the values determined using the box counting method. Brain magnetic resonance images of 30 relatively healthy persons aged 18-30 years (15 men and 15 women) were used in the study. T2-weighted digital magnetic resonance images were studied. The midsagittal MR sections of the cerebellar vermis were investigated. The caliper method in the author’s modification was used for fractal analysis. The average value of the fractal dimension of the linear contour of the cerebellum, determined using the caliper method, was 1.513±0.008 (1.432÷1.600). The average value of the fractal dimension of the linear contour of the cerebellum, determined using the box counting method, was 1.530±0.010 (1.427÷1.647). The average value of the fractal dimension of the cerebellar tissue as a whole, determined using the box counting method, was 1.760±0.006 (1.674÷1.837). The values of the fractal dimension of the outer linear contour of the cerebellum, determined using the caliper method and the box counting method were not statistically significantly different. Therefore, both methods can be used for fractal analysis of the linear contour of the cerebellum. Fractal analysis of the outer linear contour of the cerebellum allows to quantify the complexity of the spatial configuration of the outer surface of the cerebellum, which is difficult to estimate using traditional morphometric methods. The data obtained from this study and the methodology of the caliper method of fractal analysis in the author’s modification can be used for morphometric investigations of the human cerebellum in morphological studies, as well as in assessment of cerebellar MR images for diagnostic purposes.

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Root system architecture influencing cadmium accumulation in rice (Oryza sativa L.)

International Journal of Phytoremediation ◽

10.1080/15226514.2018.1523869 ◽

2019 ◽

Vol 21 (1) ◽

pp. 19-26 ◽

Cited By ~ 1

Author(s):

Weeradej Meeinkuirt ◽

Theerawut Phusantisampan ◽

Patompong Saengwilai

Keyword(s):

Oryza Sativa ◽

Root System ◽

System Architecture ◽

Oryza Sativa L ◽

Cadmium Accumulation ◽

Root System Architecture

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Fractal analysis of dendrite morphology using modified box-counting method

Neuroscience Research ◽

10.1016/j.neures.2014.04.005 ◽

2014 ◽

Vol 84 ◽

pp. 64-67 ◽

Cited By ~ 13

Author(s):

Dušan Ristanović ◽

Bratislav D. Stefanović ◽

Nela Puškaš

Keyword(s):

Fractal Analysis ◽

Counting Method ◽

Dendrite Morphology ◽

Box Counting ◽

Box Counting Method

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Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multi-trait index and Bayesian networks

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab178 ◽

2021 ◽

Author(s):

Santosh Sharma ◽

Shannon R M Pinson ◽

David R Gealy ◽

Jeremy D Edwards

Keyword(s):

Oryza Sativa ◽

Root System ◽

System Architecture ◽

Genomic Prediction ◽

Agronomic Traits ◽

Oryza Sativa L ◽

Root System Architecture ◽

Root Tips ◽

Phenotypic Data ◽

Genomic Predictions

Abstract Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 x Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction methods. We found GPAs were improved using multi-trait (BN) compared to single trait genomic prediction in traits with low to moderate heritability. Two groups of individuals were selected based on genomic predictions and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on genomic predictions did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multi-trait genomic prediction model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.

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ADAPTED BOX-COUNTING METHOD FOR ASSESSMENT OF THE FRACTAL DIMENSION OF FINANCIAL TIME SERIES

Applied Mathematics and Control Sciences ◽

10.15593/2499-9873/2020.3.10 ◽

2020 ◽

pp. 185-218

Author(s):

Robert Garafutdinov ◽

◽

Sofya Akhunyanova ◽

Keyword(s):

Time Series ◽

Fractal Dimension ◽

Fractal Analysis ◽

Computer Image ◽

Financial Time Series ◽

Counting Method ◽

Financial Time ◽

Box Counting ◽

Modeling And Prediction ◽

Box Counting Method

This paper continues research within the framework of the scientific direction in econophysics at the Department of Information Systems and Mathematical Methods in Economics of the faculty of Economics of PSU. Modeling and prediction of financial time series is quite a perspective area of research, because it allows participants of financial processes to reduce risks and make effective decisions. For example, we could research financial processes with the help of fractal analysis. In the article there is studied and worked out in detail one of the methods of fractal analysis of financial time series – the box-counting method for assessment of the fractal dimension. This method is often used in studies conducted by domestic authors, but the authors do not delve into the characteristics and problems of using the box-counting method for analysis of time series, that means that the answers to the interested questions have not yet been given. The main problem is that, as a rule, the analyzed object in the tasks of applying the box-counting method to time series is a computer image of the plot of series. In the article there is proposed the procedure of adaptation of the box-counting method for assessment of the fractal dimension of time series, the procedure does not require the formation of a computer image of the plot. In the article there is considered following difficulties developed from this adaptation: 1) high sensitivity of the resulting estimation of the dimension to the input parameters of the method (the ratio of the sides of the covered by cells plane with the plot; the used range of lengths of the side of the cell; the number of partitions of the plane into cells); 2) the non-obviousness of choosing the optimal values of these parameters. In the article there are analyzed approaches to the selection of these parameters that were proposed by other authors, and there are determined the most suitable approaches for the adapted box-counting method. Also there are developed unique methods for determining the ratio of the sides of the plane with the plot. In the paper there is written the computer program that implements the developed method, and this program is tested on the generated data. The study obtained the following results. The fact of sensitivity of the adapted box-counting method to input parameters is confirmed, that indicates the high importance of the correct choice of these parameters. According to the study, there is found out inability of the proposed methods of automatic determination the ratio of the sides of the plane in relation to artificial time series. There are obtained the most precise (in a statistical sense) estimates of fractal dimension, those found by means of the adapted box-counting method, with the fixed ratio of the sides 1:1. According to comparing the adapted box-counting method and R/S analysis, there are obtained the most precise estimates by the second method (R/S analysis). Finally in the paper there are formulated the possible directions for further research: 1) comparison of the accuracy of various methods for assessment of the fractal dimension on series of different lengths; 2) comparison of the methods of fractal analysis and p-adic analysis for modeling and prediction of financial time series; 3) determination of the conditions of applicability of various methods; 4) approbation of the developed methods for determining of the ratio of the sides of the plane with the plot on real economic data.

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