Image Perceptual Similarity Metrics for the Assessment of Basal Cell Carcinoma.

Efficient management of basal cell carcinomas (BCC) requires reliable assessments of both tumors and post-treatment scars. We aimed to estimate image similarity metrics that account for BCC's perceptual color and texture deviation from perilesional skin. In total, 176 clinical photographs of BCC were assessed by six physicians using a visual deviation scale. Internal consistency and inter-rater agreement were estimated using Cronbach's α, weighted Gwet's AC2, and quadratic Cohen's kappa. The mean visual scores were used to validate a range of similarity metrics employing different color spaces, distances, and image embeddings from a pre-trained VGG16 neural network. The calculated similarities were transformed into discrete values using ordinal logistic regression models. The Bray-Curtis distance in the YIQ color model and rectified embeddings from the 'fc6' layer minimized the mean squared error and demonstrated strong performance in representing perceptual similarities. Box plot analysis and the Wilcoxon rank-sum test were used to visualize and compare the levels of agreement, conducted on a random validation round between the two groups: 'Human-System' and 'Human-Human.' The proposed metrics were comparable in terms of internal consistency and agreement with human raters. The findings suggest that the proposed metrics offer a robust and cost-effective approach to monitoring BCC treatment outcomes in clinical settings

Quality Enhancement of 3D Models Reconstructed By RGB-D Camera Systems

Low-cost RGB-D cameras like Microsoft\u27s Kinect capture RGB data for each vertex while reconstructing 3D models from objects with obvious drawbacks of poor mesh and texture qualities due to their hardware limitations. In this thesis we propose a combined method that enhances geometrically and chromatically 3D models reconstructed by RGB-D camera systems. Our approach utilizes Butterfly Subdivision and Surface Fitting techniques to generate smoother triangle surface meshes, where sharp features can be well preserved or minimized by different Surface Fitting algorithms. Additionally the global contrast of mesh textures is enhanced by using a modified Histogram Equalization algorithm, in which the new intensity of each vertex is obtained by applying cumulative distribution function and calculating the accumulated normalized histogram of the texture. A number of experimental results and comparisons demonstrate that our method efficiently and effectively improves the geometric and chromatic quality of 3D models reconstructed from RGB-D cameras

Notre-Dame de Paris as a validation case to improve fire safety modelling in historic buildings

The analysis of the thermal damages in Notre-Dame de Paris is necessary to estimate the impact of the dramatic 2019 fire on the remaining structure prior to reconstruction. In doing so, the large amount of data being generated creates a benchmark environment to test the relevance of numerical fire models in the unconventional configuration of a medieval roof. While being an uncontrolled and complex configuration, it can provide insights regarding the relevance of numerical tools for fire risk assessment in historic buildings. Analysing the thermal degradation of the Lutetian limestone in a vault of the choir, experimental techniques are developed to track the in-depth maximum temperature profile reached during the fire. Numerical simulations of the fire development in the roof space then aim at replicating the observations through the evaluation of the heat flux impinging the vaults during the fire. These simulations are carried out using Fire Dynamic Simulator, which requires a large range of assumptions prior to any simulation regarding materials, geometry, meshing and scale. These assumptions are described and pave the way to a future sensitivity analysis to confront the upcoming outcomes of the simulations with the experimental observations

Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation

Identifying the plant's developmental growth stages from seed leaf is crucial to understand plant science and cultivation management deeply. An efficient vision-based system for plant growth monitoring entails optimum segmentation and classification algorithms. This study presents coupled color-based superpixels and multifold watershed transformation in segmenting lettuce plant from complicated background taken from smart farm aquaponic system, and machine learning models used to classify lettuce plant growth as vegetative, head development and for harvest based on phytomorphological profile. Morphological computations were employed by feature extraction of the number of leaves, biomass area and perimeter, convex area, convex hull area and perimeter, major and minor axis lengths of the major axis length the dominant leaf, and length of plant skeleton. Phytomorphological variations of biomass compactness, convexity, solidity, plant skeleton, and perimeter ratio were included as inputs of the classification network. The extracted Lab color space information from the training image set undergoes superpixels overlaying with 1,000 superpixel regions employing K-means clustering on each pixel class. Six-level watershed transformation with distance transformation and minima imposition was employed to segment the lettuce plant from other pixel objects. The accuracy of correctly classifying the vegetative, head development, and harvest growth stages are 88.89%, 86.67%, and 79.63%, respectively. The experiment shows that the test accuracy rates of machine learning models were recorded as 60% for LDA, 85% for ANN, and 88.33% for QSVM. Comparative analysis showed that QSVM bested the performance of optimized LDA and ANN in classifying lettuce growth stages. This research developed a seamless model in segmenting vegetation pixels, and predicting lettuce growth stage is essential for plant computational phenotyping and agricultural practice optimization

Thermal signatures of skin lesions using computational thermal modeling and medical infrared imaging

Infrared (IR) thermography is a valuable quantitative diagnostic tool that allows for non-invasive, accurate measurement of skin temperature variations in the presence of a lesion. Modeling the underlying thermal and physiological processes within the body offers excellent potential for improving the thermographic measurement system design and developing more exact, quantitative assessment criteria. Using computational modeling and infrared imaging experiments, this dissertation investigates the thermal signatures of lesions of varying geometrical and physiological characteristics. We first performed a comprehensive sensitivity analysis of the computed skin temperatures in order to understand the relationships between healthy skin temperatures and the underlying thermophysical processes and tissue properties. These functional relationships provide a foundation for interpreting steady state and transient thermal signatures of skin lesions. We developed a computational thermal model for a heel deep tissue injury (DTI) to allow for an early thermographic detection and assessment capability for DTIs. The DTI models were used to develop thermographic measurement strategies and quantitative staging criteria that can be employed in a clinical setting. We analyzed the infrared images of various vascular tumors and pigmented skin lesions acquired from patient studies, using the combined white light-infrared image processing approaches. Our quantitative thermal analysis of lesions of different physiological characteristics, sizes, locations and depths will facilitate quantitative assessment and interpretation of other skin lesion thermographic images. A better understanding of the thermal behavior of skin lesions, gained using computational modeling and infrared imaging experiments in this study, can contribute to the advanced use of quantitative infrared imaging in medical diagnostic applications

Large scale phenotyping and molecular analysis in a germplasm collection of rocket salad (Eruca vesicaria) reveal a differentiation of the gene pool by geographical origin

[EN] Cultivated rocket (Eruca vesicaria) is a leafy vegetable highly appreciated for its healthpromoting virtues and consumed both raw and cooked as ready-to-use vegetable. Despite Eruca being cultivated worldwide, only a few cultivars are available and limited breeding activities have been carried out so far. Therefore, the genetic resources available represent an unexploited potential source of variation for breeding. In the present study, 155 E. vesicaria accessions from 30 countries across Europe, Asia, Africa, and America have been characterized for 54 qualitative and quantitative morphological and quality traits. Conventional descriptors and automated tools for the determination of the quality, morphology, and colour of leaves have been used. Genetic diversity was assessed using 15 inter simple sequence repeat and simple sequence repeat markers. A high level of diversity was evidenced in the collection. Significant differences were found in most of the traits with the exception of five pseudo-qualitative descriptors. The first and second dimensions of the principal components analysis with phenotypic traits accounted for 25.69% of total variation showing a stratification of the genotypes according to the European and Asian origins. In total, 75% of the variation was contained in the first 15 components having eigenvalues higher than 1.0. Also, the population structure divided the collection into two main clusters separating European genotypes from the rest. Furthermore, hierarchical cluster analysis confirmed a geographical separation, grouping the accessions into three major clusters, which were differentiated by plant architecture, leaf and flower colour, leaf water status, leaf blade shape and hairiness of the leaves and stem. Our approach has broadened the knowledge of the diversity within the Eruca gene pool, thus contributing to identify sources of variation and to select the best candidates for cultivated rocket breeding programs, as well as to determine the genetic basis of plant and leaf traits in future genome-wide association studies.The work was supported by 'RGV-FAO' project funded by the Italian Ministry of Agriculture, Food and Forestry. C. Guijarro-Real thanks the Ministerio de Educacion, Cultura y Deporte of Spain (MECD) for its financial support by means of a predoctoral FPU Grant (FPU14/06798), and for the specific grant for mobility (EST17/00354) from the same Organization.Guijarro-Real, C.; Navarro, A.; Esposito, S.; Festa, G.; Macellaro, R.; Di Cesare, C.; Fita, A.... (2020). Large scale phenotyping and molecular analysis in a germplasm collection of rocket salad (Eruca vesicaria) reveal a differentiation of the gene pool by geographical origin. 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Following Darwin's footsteps using 'the most wonderful plants in the world': the ecophysiological responses of the carnivorous plant Drosera rotundifolia to nitrogen availability.

Nitrogen (N) is an essential element to plants for growth, maintenance and reproduction, however most N does not exist in a form that is biologically available to plants. In order to maximise the acquisition and retention of N, plants have evolved a variety of morphological and physiological adaptations and life history strategies, as well as the ability to respond plastically to changes in resource availability in ecological time. Determining the ecophysiological responses of plants to changes in root N availability is crucial to further understanding of the mechanisms underlying competitive interactions between plants, and between plants and other organisms, that ultimately contribute to community structure and ecosystem functioning. Carnivorous plants are ideal systems for investigating ecophysiological responses to N availability as:- (i) they share a unique adaptation for obtaining supplemental N from captured prey, therefore ecological stoichiometry and energetic cost/benefit models may be explored; (ii) the trait of botanical carnivory is widely considered to have independently co-evolved as a response to N-deficient, sunny and wet environments, therefore resource allocation trade-offs between plant investment in N and carbon (C) acquisition may be observed, and (iii) they are extremely sensitive to changes in root N availability in ecological time. In this research, the carnivorous plant Drosera rotundifolia (round-leaved sundew) was used to address several unanswered ecophysiological and evolutionary questions relating to patterns and processes of prey capture and the N nutrition of carnivorous plants. Furthermore, the potential for reducing uncertainty in the calculation of plant reliance on carnivory using a δ15N natural abundance multi-level linear mixing model was explored. A combined approach of in-situ and ex-situ studies was employed, using co-occurring non-carnivorous plants or carnivorous plant species with differing evolutionary lineages or prey capture mechanisms respectively to provide context. Results show that the adaptations of carnivory, high reproductive investment and a relatively short life span enable Drosera rotundifolia to survive and thrive in an extreme, N deficient environment. Phenotypically plastic responses by the plant to light and root N availability provide evidence of resource allocation trade-offs between investment in carnivory for N acquisition and in photosynthesis for C acquisition. Plants invested less heavily in prey capture (measured as the stickiness of leaf mucilage) as N availability increased or light availability decreased. These results show that the energetic costs associated with carnivory are avoided by the plant when less costly sources of N are available for uptake and that the production of carbon-rich mucilage is only made under nutrient-limited and well-lit conditions. Results obtained from the comparison of captured insect prey with background invertebrates of potential prey indicate that Drosera rotundifolia is a dietary generalist, where the quantity of prey captured per plant is positively correlated with leaf stickiness and total leaf area. Plant reliance on prey-derived N decreased with increasing root N availability, providing evidence that carnivory is only of net benefit to the plant in N-deficient and well-lit environments, as the photosynthetic costs of investment in the trait are not exceeded by the energetic gain from prey N uptake in shady or dry habitats. A more accurate and precise method for calculating plant reliance on botanical carnivory is presented which incorporates the insect diet of the plant. This method has wider significance for reducing uncertainty in the calculation of relative source contributions to a mixture for most natural abundance applications using a multi-level linear mixing model. To conclude, results from this research further understanding of the ecophysiological mechanisms underlying plant responses to changes in resource availability and the selective pressures driving the evolution of plant adaptations. These results therefore assist with predicting how plants and plant communities may respond to sustained N deposition inputs and future environmental scenarios

Microbial Diversity as a Bioindicator of the Impact of Fires

Wildfires are a major problem in Galicia and frequency and severity is increasing due to climate change. Prescribed burning is widely used as a management tool to prevent high severity fire but information about the effect of repetitive burning on soil quality is scarce. The effectiveness of post-fire rehabilitation techniques on reducing soil erosion is evident, but its effect on soil properties is unknown. The aim of this study is to assess the impact of fires on soil, considering fire severity and recurrence, and to evaluate the use of microbial parameters as indicators of these changes. Moreover, it aims to analyse the effect of straw mulching and seeding on soil quality and vegetation recovery

Reconstruction of 3D scenes from pairs of uncalibrated images. Creation of an interactive system for extracting 3D data points and investigation of automatic techniques for generating dense 3D data maps from pairs of uncalibrated images for remote sensing applications.

Much research effort has been devoted to producing algorithms that contribute directly or indirectly to the extraction of 3D information from a wide variety of types of scenes and conditions of image capture. The research work presented in this thesis is aimed at three distinct applications in this area: interactively extracting 3D points from a pair of uncalibrated images in a flexible way; finding corresponding points automatically in high resolution images, particularly those of archaeological scenes captured from a freely moving light aircraft; and improving a correlation approach to dense disparity mapping leading to 3D surface reconstructions. The fundamental concepts required to describe the principles of stereo vision, the camera models, and the epipolar geometry described by the fundamental matrix are introduced, followed by a detailed literature review of existing methods. An interactive system for viewing a scene via a monochrome or colour anaglyph is presented which allows the user to choose the level of compromise between amount of colour and ghosting perceived by controlling colour saturation, and to choose the depth plane of interest. An improved method of extracting 3D coordinates from disparity values when there is significant error is presented. Interactive methods, while very flexible, require significant effort from the user finding and fusing corresponding points and the thesis continues by presenting several variants of existing scale invariant feature transform methods to automatically find correspondences in uncalibrated high resolution aerial images with improved speed and memory requirements. In addition, a contribution to estimating lens distortion correction by a Levenberg Marquard based method is presented; generating data strings for straight lines which are essential input for estimating lens distortion correction. The remainder of the thesis presents correlation based methods for generating dense disparity maps based on single and multiple image rectifications using sets of automatically found correspondences and demonstrates improvements obtained using the latter method. Some example views of point clouds for 3D surfaces produced from pairs of uncalibrated images using the methods presented in the thesis are included.Al-Baath UniversityThe appendices files and images are not available online