3D multimodal simulation of image acquisition by X-Ray and MRI for validation of seedling measurements with segmentation algorithms

3D multimodal simulation of image acquisition by X-Ray and MRI for validation of seedling measurements with segmentation algorithms

Biomedical Sensing and Imaging

This book mainly deals with recent advances in biomedical sensing and imaging. More recently, wearable/smart biosensors and devices, which facilitate diagnostics in a non-clinical setting, have become a hot topic. Combined with machine learning and artificial intelligence, they could revolutionize the biomedical diagnostic field. The aim of this book is to provide a research forum in biomedical sensing and imaging and extend the scientific frontier of this very important and significant biomedical endeavor

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time

Variations in root system architecture and root growth dynamics of Brassica rapa genotypes using a new scanner-based phenotyping system

There is a need to breed for root systems architectures (RSAs) that optimise soil resource acquisition. This requires high resolution and high-throughput quantification of RSA in as natural an environment as possible. Current imaging techniques are limited by cost, reproducibility, throughput and complexity. This thesis describes (1) the construction of a low cost, high-resolution, root phenotyping platform that requires no sophisticated equipment which is adaptable to most laboratory and glasshouse environments and (2) its application to quantify environmental and temporal variation in RSA between genotypes of Brassica rapa L. The high resolution root phenotyping system (HRP) that was constructed employed 24 scanners and could screen up to 72 individual plants at any time, with the possibility of capturing thousands of root images daily depending on the operational number of scanners and scanning periodicity. Plants were supplied with a complete nutrient solution through the wick of a germination paper. Images of RSA were acquired automatically, over extended periods, using multiple scanners controlled by customised software. The RSA data was used to validate a mechanistic model and mixed effects models were used to describe the sources of variation in traits contributing to RSA. Plants were also grown in rhizoboxes and under varying concentrations of P ([P]ext). Broad-sense heritability (H2), was highest (≥ 0.70) for shoot biomass, length of primary roots (PRs), number of lateral roots (LRs). Coefficients of variation in RSA traits within a genotype were large and ranged between 5 and 103%. It was found that between 4 and 48 replicates were needed to detect a significant difference (95% CI, 50% difference between trait means). Significant differences were found between genotypes in root traits with strong positive correlations among RSA traits and between biomass and RSA traits. Principal component analyses identified 5 significant axes of variation, accounting for approximately 86 and 78% of the variation in the genotypes on paper and soil substrates, respectively. Cluster analysis of the genotypes produced 5 discrete groups. Genotypes with more or less shoot biomass or with bigger or smaller RSA could be distinguished. A density-based 2D model reproduced experimental results accurately by simulating PR length and total length of LRs. Mixed-effects statistical models demonstrated that root traits show temporal variations of various types with significant effects of genotype. All genotypes followed a similar growth pattern with time, but differed in their maximum total root length (TRL), primary root length (PRL) and LR growth. A 3-parameter logistic model satisfactorily described TRL and PRL when genotypes were grown on both paper and soil substrates. On paper substrate, TRL required only a single, random-effect parameter (asymptote), describing maximum TRL. On soil substrate, TRL required two random-effects parameters, asymptote and inflection, describing maximum TRL and time at which ½ of maximum TRL occurs, respectively. Primary root length on both paper and soil substrates required only a single, random-effect parameter, describing maximum PRL. The growth rate of LRs of all ages followed a quadratic function and required only a single, random-effect parameter, describing maximum growth rate. There was variation in specific RSA traits and plasticity in response to [P]ext among genotypes. Length of the apical un-branched zone of the PR increased with increasing [P]ext. Total root length, total LR length and number of LRs was positively correlated with total plant tissue P concentration at low [P]ext but not at high [P]ext. Paper substrate was more suitable for screening seedling root traits but root phenotypes must be validated in situ in the field or in soil media because some differences were evident between data observed on paper and soil substrates. Scanner-based phenotyping of RSA provides economical means of studying the mechanisms underlying the plant-soil interactions and can be used to quantify environmental and temporal variation in traits contributing to RSA. The HRP system can be extended to screen the large populations required for breeding for efficient resource acquisition. The necessity for high replication and time-consuming image analysis could however limit throughput in the phenotyping system

Variations in root system architecture and root growth dynamics of Brassica rapa genotypes using a new scanner-based phenotyping system

There is a need to breed for root systems architectures (RSAs) that optimise soil resource acquisition. This requires high resolution and high-throughput quantification of RSA in as natural an environment as possible. Current imaging techniques are limited by cost, reproducibility, throughput and complexity. This thesis describes (1) the construction of a low cost, high-resolution, root phenotyping platform that requires no sophisticated equipment which is adaptable to most laboratory and glasshouse environments and (2) its application to quantify environmental and temporal variation in RSA between genotypes of Brassica rapa L. The high resolution root phenotyping system (HRP) that was constructed employed 24 scanners and could screen up to 72 individual plants at any time, with the possibility of capturing thousands of root images daily depending on the operational number of scanners and scanning periodicity. Plants were supplied with a complete nutrient solution through the wick of a germination paper. Images of RSA were acquired automatically, over extended periods, using multiple scanners controlled by customised software. The RSA data was used to validate a mechanistic model and mixed effects models were used to describe the sources of variation in traits contributing to RSA. Plants were also grown in rhizoboxes and under varying concentrations of P ([P]ext). Broad-sense heritability (H2), was highest (≥ 0.70) for shoot biomass, length of primary roots (PRs), number of lateral roots (LRs). Coefficients of variation in RSA traits within a genotype were large and ranged between 5 and 103%. It was found that between 4 and 48 replicates were needed to detect a significant difference (95% CI, 50% difference between trait means). Significant differences were found between genotypes in root traits with strong positive correlations among RSA traits and between biomass and RSA traits. Principal component analyses identified 5 significant axes of variation, accounting for approximately 86 and 78% of the variation in the genotypes on paper and soil substrates, respectively. Cluster analysis of the genotypes produced 5 discrete groups. Genotypes with more or less shoot biomass or with bigger or smaller RSA could be distinguished. A density-based 2D model reproduced experimental results accurately by simulating PR length and total length of LRs. Mixed-effects statistical models demonstrated that root traits show temporal variations of various types with significant effects of genotype. All genotypes followed a similar growth pattern with time, but differed in their maximum total root length (TRL), primary root length (PRL) and LR growth. A 3-parameter logistic model satisfactorily described TRL and PRL when genotypes were grown on both paper and soil substrates. On paper substrate, TRL required only a single, random-effect parameter (asymptote), describing maximum TRL. On soil substrate, TRL required two random-effects parameters, asymptote and inflection, describing maximum TRL and time at which ½ of maximum TRL occurs, respectively. Primary root length on both paper and soil substrates required only a single, random-effect parameter, describing maximum PRL. The growth rate of LRs of all ages followed a quadratic function and required only a single, random-effect parameter, describing maximum growth rate. There was variation in specific RSA traits and plasticity in response to [P]ext among genotypes. Length of the apical un-branched zone of the PR increased with increasing [P]ext. Total root length, total LR length and number of LRs was positively correlated with total plant tissue P concentration at low [P]ext but not at high [P]ext. Paper substrate was more suitable for screening seedling root traits but root phenotypes must be validated in situ in the field or in soil media because some differences were evident between data observed on paper and soil substrates. Scanner-based phenotyping of RSA provides economical means of studying the mechanisms underlying the plant-soil interactions and can be used to quantify environmental and temporal variation in traits contributing to RSA. The HRP system can be extended to screen the large populations required for breeding for efficient resource acquisition. The necessity for high replication and time-consuming image analysis could however limit throughput in the phenotyping system

Simulation Modeling

The book presents some recent specialized works of a theoretical and practical nature in the field of simulation modeling, which is being addressed to a large number of specialists, mathematicians, doctors, engineers, economists, professors, and students. The book comprises 11 chapters that promote modern mathematical algorithms and simulation modeling techniques, in practical applications, in the following thematic areas: mathematics, biomedicine, systems of systems, materials science and engineering, energy systems, and economics. This project presents scientific papers and applications that emphasize the capabilities of simulation modeling methods, helping readers to understand the phenomena that take place in the real world, the conditions of their development, and their effects, at a high scientific and technical level. The authors have published work examples and case studies that resulted from their researches in the field. The readers get new solutions and answers to questions related to the emerging applications of simulation modeling and their advantages

Visual and Camera Sensors

This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors

Design and Development of Robotic Part Assembly System under Vision Guidance

Robots are widely used for part assembly across manufacturing industries to attain high productivity through automation. The automated mechanical part assembly system contributes a major share in production process. An appropriate vision guided robotic assembly system further minimizes the lead time and improve quality of the end product by suitable object detection methods and robot control strategies. An approach is made for the development of robotic part assembly system with the aid of industrial vision system. This approach is accomplished mainly in three phases. The first phase of research is mainly focused on feature extraction and object detection techniques. A hybrid edge detection method is developed by combining both fuzzy inference rule and wavelet transformation. The performance of this edge detector is quantitatively analysed and compared with widely used edge detectors like Canny, Sobel, Prewitt, mathematical morphology based, Robert, Laplacian of Gaussian and wavelet transformation based. A comparative study is performed for choosing a suitable corner detection method. The corner detection technique used in the study are curvature scale space, Wang-Brady and Harris method. The successful implementation of vision guided robotic system is dependent on the system configuration like eye-in-hand or eye-to-hand. In this configuration, there may be a case that the captured images of the parts is corrupted by geometric transformation such as scaling, rotation, translation and blurring due to camera or robot motion. Considering such issue, an image reconstruction method is proposed by using orthogonal Zernike moment invariants. The suggested method uses a selection process of moment order to reconstruct the affected image. This enables the object detection method efficient. In the second phase, the proposed system is developed by integrating the vision system and robot system. The proposed feature extraction and object detection methods are tested and found efficient for the purpose. In the third stage, robot navigation based on visual feedback are proposed. In the control scheme, general moment invariants, Legendre moment and Zernike moment invariants are used. The selection of best combination of visual features are performed by measuring the hamming distance between all possible combinations of visual features. This results in finding the best combination that makes the image based visual servoing control efficient. An indirect method is employed in determining the moment invariants for Legendre moment and Zernike moment. These moments are used as they are robust to noise. The control laws, based on these three global feature of image, perform efficiently to navigate the robot in the desire environment