Development and evaluation of a digital tool for virtual reconstruction of historic Islamic geometric patterns

For the purpose of cultural heritage preservation, the task of recording and reconstructing visually complicated architectural geometrical patterns is facing many practical challenges. Existing traditional technologies rely heavily on the subjective nature of our perceptual power in understanding its complexity and depicting its color differences. This study explores one possible solution, through utilizing digital techniques for reconstructing detailed historical Islamic geometric patterns. Its main hypothesis is that digital techniques offer many advantages over the human eye in terms of recognizing subtle differences in light and color. The objective of the study is to design, test and evaluate an automatic visual tool for identifying deteriorated or incomplete archaeological Islamic geometrical patterns captured in digital images, and then restoring them digitally, for the purpose of producing accurate 2D reconstructed metric models. An experimental approach is used to develop, test and evaluate the specialized software. The goal of the experiment is to analyze the output reconstructed patterns for the purpose of evaluating the digital tool in respect to reliability and structural accuracy, from the point of view of the researcher in the context of historic preservation. The research encapsulates two approaches within its methodology; Qualitative approach is evident in the process of program design, algorithm selection, and evaluation. Quantitative approach is manifested through using mathematical knowledge of pattern generation to interpret available data and to simulate the rest based on it. The reconstruction process involves induction, deduction and analogy. The proposed method was proven to be successful in capturing the accurate structural geometry of the deteriorated straight-lines patterns generated based on the octagon-square basic grid. This research also concluded that it is possible to apply the same conceptual method to reconstruct all two-dimensional Islamic geometric patterns. Moreover, the same methodology can be applied to reconstruct many other pattern systems. The conceptual framework proposed by this study can serve as a platform for developing professional softwares related to historic documentation. Future research should be directed more towards developing artificial intelligence and pattern recognition techniques that have the ability to suplement human power in accomplishing difficult tasks

In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging

La osteoporosis es una enfermedad ósea que se manifiesta con una menor densidad ósea y el deterioro de la arquitectura del hueso esponjoso. Ambos factores aumentan la fragilidad ósea y el riesgo de sufrir fracturas óseas, especialmente en mujeres, donde existe una alta prevalencia. El diagnóstico actual de la osteoporosis se basa en la cuantificación de la densidad mineral ósea (DMO) mediante la técnica de absorciometría dual de rayos X (DXA). Sin embargo, la DMO no puede considerarse de manera aislada para la evaluación del riesgo de fractura o los efectos terapéuticos. Existen otros factores, tales como la disposición microestructural de las trabéculas y sus características que es necesario tener en cuenta para determinar la calidad del hueso y evaluar de manera más directa el riesgo de fractura. Los avances técnicos de las modalidades de imagen médica, como la tomografía computarizada multidetector (MDCT), la tomografía computarizada periférica cuantitativa (HR-pQCT) y la resonancia magnética (RM) han permitido la adquisición in vivo con resoluciones espaciales elevadas. La estructura del hueso trabecular puede observarse con un buen detalle empleando estas técnicas. En particular, el uso de los equipos de RM de 3 Teslas (T) ha permitido la adquisición con resoluciones espaciales muy altas. Además, el buen contraste entre hueso y médula que proporcionan las imágenes de RM, así como la utilización de radiaciones no ionizantes sitúan a la RM como una técnica muy adecuada para la caracterización in vivo de hueso trabecular en la enfermedad de la osteoporosis. En la presente tesis se proponen nuevos desarrollos metodológicos para la caracterización morfométrica y mecánica del hueso trabecular en tres dimensiones (3D) y se aplican a adquisiciones de RM de 3T con alta resolución espacial. El análisis morfométrico está compuesto por diferentes algoritmos diseñados para cuantificar la morfología, la complejidad, la topología y los parámetros de anisotropía del tejido trabecular. En cuanto a la caracterización mecánica, se desarrollaron nuevos métodos que permiten la simulación automatizada de la estructura del hueso trabecular en condiciones de compresión y el cálculo del módulo de elasticidad. La metodología desarrollada se ha aplicado a una población de sujetos sanos con el fin de obtener los valores de normalidad del hueso esponjoso. Los algoritmos se han aplicado también a una población de pacientes con osteoporosis con el fin de cuantificar las variaciones de los parámetros en la enfermedad y evaluar las diferencias con los resultados obtenidos en un grupo de sujetos sanos con edad similar.Los desarrollos metodológicos propuestos y las aplicaciones clínicas proporcionan resultados satisfactorios, presentando los parámetros una alta sensibilidad a variaciones de la estructura trabecular principalmente influenciadas por el sexo y el estado de enfermedad. Por otra parte, los métodos presentan elevada reproducibilidad y precisión en la cuantificación de los valores morfométricos y mecánicos. Estos resultados refuerzan el uso de los parámetros presentados como posibles biomarcadores de imagen en la enfermedad de la osteoporosis.Alberich Bayarri, Á. (2010). In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8981Palanci

Development and evaluation of a digital tool for virtual reconstruction of historic Islamic geometric patterns

For the purpose of cultural heritage preservation, the task of recording and reconstructing visually complicated architectural geometrical patterns is facing many practical challenges. Existing traditional technologies rely heavily on the subjective nature of our perceptual power in understanding its complexity and depicting its color differences. This study explores one possible solution, through utilizing digital techniques for reconstructing detailed historical Islamic geometric patterns. Its main hypothesis is that digital techniques offer many advantages over the human eye in terms of recognizing subtle differences in light and color. The objective of the study is to design, test and evaluate an automatic visual tool for identifying deteriorated or incomplete archaeological Islamic geometrical patterns captured in digital images, and then restoring them digitally, for the purpose of producing accurate 2D reconstructed metric models. An experimental approach is used to develop, test and evaluate the specialized software. The goal of the experiment is to analyze the output reconstructed patterns for the purpose of evaluating the digital tool in respect to reliability and structural accuracy, from the point of view of the researcher in the context of historic preservation. The research encapsulates two approaches within its methodology; Qualitative approach is evident in the process of program design, algorithm selection, and evaluation. Quantitative approach is manifested through using mathematical knowledge of pattern generation to interpret available data and to simulate the rest based on it. The reconstruction process involves induction, deduction and analogy. The proposed method was proven to be successful in capturing the accurate structural geometry of the deteriorated straight-lines patterns generated based on the octagon-square basic grid. This research also concluded that it is possible to apply the same conceptual method to reconstruct all two-dimensional Islamic geometric patterns. Moreover, the same methodology can be applied to reconstruct many other pattern systems. The conceptual framework proposed by this study can serve as a platform for developing professional softwares related to historic documentation. Future research should be directed more towards developing artificial intelligence and pattern recognition techniques that have the ability to suplement human power in accomplishing difficult tasks

Automated Computational Techniques for High-throughput Image Analysis of Skin Structure

Biological image processing and analysis are concerned with enhancing and quantifying features that reflect different pathological states, based on the use of combinations of image processing algorithms. The integration of image processing and analysis techniques to evaluate and assess skin integrity in both human and mouse models is a major theme in this thesis. More specifically, this thesis describes computational systems for high-throughput analysis of skin tissue section images and non-invasive imaging techniques. As the skin is a largest organ in the mammalian body, and is complex in structure, manual quantification and analysis a hard task for the observer to determine an objective result, and furthermore, the analysis is complex in terms of accuracy and time taken. To look at the gross morphology of the skin, I developed high throughput analysis based on an adaptive active contour model to isolate the skin layers and provide quantification methods. This was utilised in a study to evaluate cutaneous morphology in 475 knockout mouse lines provided by the Mouse Genetics Project (MGP) pipeline, that was generated by the Wellcome Trust Sanger Institute (WTSI). This is a major international initiative to provide both functional annotation of the mammalian genome and insight into the genetic basis of disease. I found 53 interesting adipocyte phenotypes, 18 interesting dermal phenotypes and 3 interesting epidermal phenotypes. I also focussed on the analysis of collagen in the dermis of skin images in several ways. For collagen structure analysis, I developed a combined system of Gabor filtering and Fast Fourier Transform FFT. This analysis allowed the detection of subtle changes in collagen organisation. Using similar images, I also measured collagen bundle thickness by computing the maximum frequency of the FFT power spectrum. To assess collagen dynamics, I developed k-means clustering for segmentation based on colour distribution. The use of this approach allowed the measurement of dermal degradation with age and disease, which was not possible by existing means. Obtaining human skin material to facilitate the drug discovery and development process is not an easy task. The manipulation, monitoring and cost of human subjects makes the use of mouse models more suitable for high-throughput screening. Therefore, I have evaluated skin integrity from mouse tissue rather than human skin, however, mouse skin is thinner than human skin and many morphological features are easier to visualise in human skin, which has implications for analysis. Skin moulds can be used to create an impression of the skin surface. Changes in texture of skin can reflect skin conditions. I developed a skin surface structure analysis system to measure the degree of change in texture of the human skin surface. The alterations detected in texture parameters in skin mould impressions reflected changes caused by sun exposure, ageing and many other clinical parameters. I compared my analysis with the existing Beagley-Gibson scoring system to find correlations between automated and manual analysis to inform a decision on the use of optimal methods. By removing subjectivity of manual methods, I was able to develop a robust system to evaluate, for example, damage resulting from UV exposure. My experimental analysis indicated that techniques developed in this thesis were able to analyse both histological samples and skin surface images in high-throughput experiments. They could, therefore, make a contribution to biological image analysis by providing accurate results to help clinical decision making, and facilitate biological laboratory experiments to improve the quality of research in this field, and save time. Overall, my thesis demonstrated that accurate analysis of the skin to gain meaningful biological information requires an automated system that can achieve feature extraction, quantification, analysis and decision making to find interesting phenotypes and abnormalities. This will help the evaluation of the effects of a specific treatment, and answer many biological questions in fields of cosmetic dermatology and drug discovery, and improve our understanding of the genetic basis of disease

Nondestructive Testing Methods and New Applications

Nondestructive testing enables scientists and engineers to evaluate the integrity of their structures and the properties of their materials or components non-intrusively, and in some instances in real-time fashion. Applying the Nondestructive techniques and modalities offers valuable savings and guarantees the quality of engineered systems and products. This technology can be employed through different modalities that include contact methods such as ultrasonic, eddy current, magnetic particles, and liquid penetrant, in addition to contact-less methods such as in thermography, radiography, and shearography. This book seeks to introduce some of the Nondestructive testing methods from its theoretical fundamentals to its specific applications. Additionally, the text contains several novel implementations of such techniques in different fields, including the assessment of civil structures (concrete) to its application in medicine

Shape segmentation and retrieval based on the skeleton cut space

3D vormverzamelingen groeien snel in veel toepassingsgebieden. Om deze effectief te kunnen gebruiken bij modelleren, simuleren, of 3D contentontwikkeling moet men 3D vormen verwerken. Voorbeelden hiervan zijn het snijden van een vorm in zijn natuurlijke onderdelen (ook bekend als segmentatie), en het vinden van vormen die lijken op een gegeven model in een grote vormverzameling (ook bekend als opvraging). Dit proefschrift presenteert nieuwe methodes voor 3D vormsegmentatie en vormopvraging die gebaseerd zijn op het zogenaamde oppervlakskelet van een 3D vorm. Hoewel allang bekend, dergelijke skeletten kunnen alleen sinds kort snel, robuust, en bijna automatisch berekend worden. Deze ontwikkelingen stellen ons in staat om oppervlakskeletten te gebruiken om vormen te karakteriseren en analyseren zodat operaties zoals segmentatie en opvraging snel en automatisch gedaan kunnen worden. We vergelijken onze nieuwe methodes met moderne methodes voor dezelfde doeleinden en laten zien dat ons aanpak kwalitatief betere resultaten kan produceren. Ten slotte presenteren wij een nieuwe methode om oppervlakskeletten te extraheren die is veel simpeler dan, en heeft vergelijkbare snelheid met, de beste technieken in zijn klasse. Samenvattend, dit proefschrift laat zien hoe men een complete workflow kan implementeren voor het segmenteren en opvragen van 3D vormen gebruik makend van oppervlakskeletten alleen

Image-based Modeling of Flow through Porous Media: Development of Multiscale Techniques for the Pore Level

Increasingly, imaging technology allows porous media problems to be modeled at microscopic and sub-microscopic levels with finer resolution. However, the physical domain size required to be representative of the media prohibits comprehensive micro-scale simulation. A hybrid or multiscale approach is necessary to overcome this challenge. In this work, a technique was developed for determining the characteristic scales of porous materials, and a multiscale modeling methodology was developed to better understand the interaction/dependence of phenomena occurring at different microscopic scales. The multiscale method couples microscopic simulations at the pore and sub-pore scales. Network modeling is a common pore-scale technique which employs severe assumptions, making it more computationally efficient than direct numerical simulation, enabling simulation over larger length scales. However, microscopic features of the medium are lost in the discretization of a material into a network of interconnected pores and throats. In contrast, detailed microstructure and flow patterns can be captured by modern meshing and direct numerical simulation techniques, but these models are computationally expensive. In this study, a data-driven multiscale technique has been developed that couples the two types of models, taking advantage of the benefits of each. Specifically, an image-based physically-representative pore network model is coupled to an FEM (finite element method) solver that operates on unstructured meshes capable of resolving details orders of magnitude smaller than the pore size. In addition to allowing simulation at multiple scales, the current implementation couples the models using a machine learning approach, where results from the FEM model are used to learn network model parameters. Examples of the model operating on real materials are given that demonstrate improvements in network modeling enabled by the multiscale framework. The framework enables more advanced multiscale and multiphysics modeling – an application to particle straining problems is shown. More realistic network filtration simulations are possible by incorporating information from the sub-pore-scale. New insights into the size exclusion mechanism of particulate filtration were gained in the process of generating data for machine learning of conductivity reduction due to particle trapping. Additional tests are required to validate the multiscale network filtration model, and compare with experimental findings in literature

Modelling Plant Variety Dependent Least Limiting Water Range (LLWR)

Drought stress is a major limiting factor for yield on a global scale (Solh and van Ginkel, 2014), with drought effects being predicted to become more severe with increasing global temperatures (IPCC, 2014). Climate change is also expected to increase the frequency and severity of floods leading to root oxygen stress (Trenberth, 2011). At the same time, current agricultural practises are increasingly relying on heavy machinery leading to soil compaction and changes in soil structure (Chamen et al., 2003), reducing the rate of cell division in the root meristem, and decreasing cell expansion (Bengough and Mullins, 1990). As such, in order to reduce yield losses it is essential to understand the complex interaction between oxygen stress, water stress and mechanical stress (Mohammadi et al., 2010). The least limiting water range (LLWR) is one such model which relates the above-mentioned soil stressors in order to estimate the soil moisture range in a particular soil for which plants should be less limited in terms of growth. However, the extent to which the LLWR considers the influence of root traits in changing its boundaries is currently limited. In order to be able to assess the effects of root trait variability on the LLWR boundaries while manipulating the LLWR soil stressors a minirhizotron based system (RS) was developed. This cheap (~£10 per unit), acrylic based, A3 sized system enabled in situ imaging of roots and root hairs. Destructive sampling methods were also used to determine root border cell numbers and root tip geometry. To further optimise the process of data collection, Rcpp based image processing algorithms were developed to obtain automated estimates of the root traits of root length, root hair, root border cells and root tip eccentricity to further increase the efficiency of the RS phenotyping platform. To test how contrasting root traits influence the LLWR a plant phenotyping experiment was performed comparing four spring barley (Hordeum vulgare L.) varieties, Optic, KWS Sassy, Derkado and Golden Promise. Root growth rates both in the vertical and horizontal directions all increased with increasing water availability and decreasing substrate density. Root hair area did not vary significantly among treatments and between variaties. Root border cell count and root tip eccentricity increased with increasing substrate density but did not vary significantly across varieties. A root micro-trait based linear interaction model was developed to describe average root growth rates and it was demonstrated that root growth rates on average follow a linear patern for values >= 8 mm day-1. Root micro-traits mostly failed to correlate well with root growth rates except for a negative assosiation with root tip geometry (cor = -0.4192, p = 2e-05**)