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The combinatorial interpretation of the persistence diagram as a Möbius inversion was recently shown to be functorial. We employ this discovery to recast the Persistent Homology Transform of a geometric complex as a representation of a cellulation on $ \mathbb{S}^n $ to the category of combinatorial persistence diagrams. Detailed examples are provided. We hope this recasting of the PH transform will allow for the adoption of existing methods from algebraic and topological combinatorics to the study of shapes. [ABSTRACT FROM AUTHOR]

The primary objective of this study was to demonstrate the potential of digital image analysis as a tool to identify microplastic (MP) particles in surface waters and to facilitate their characterisation in terms of 2D and 3D morphology. Digital image analysis preceded by microscopic analysis was used for an exhaustive quantitative and qualitative evaluation of MPs isolated from the Vistula River. Using image processing procedures, 2D and 3D shape descriptors were determined. Principal Component Analysis was used to interpret the relationships between the parameters studied, characterising MP particle geometry, type and colour. This multivariate analysis of the data allowed three or four main factors to be extracted, explaining approximately 90% of the variation in the data characterising MP morphology. It was found that the first principal component for granules, flakes and films was largely represented by strongly correlated with 2D shape descriptors (area, perimeter, equivalent area diameter) and 3D shape descriptors (Corey Shape Factor, Compactness, Dimensionality). Considering the scraps, principal component PC1 was represented by only five of the above descriptors, and the Compactness variable had the largest contribution to principal component PC2. In addition, for granules, flakes and films, a relationship between 2D shape and the colour of their particles could be observed. For the most numerous MP group identified of multicoloured scraps, no such association was found. The results of our study can be used for further multivariate analysis regarding the presence of microplastic floating on the river surface, with a particular focus on particles of secondary origin. This is of key importance for optimising future efforts in conducting small-scale and multidimensional monitoring of and reducing plastics in the aquatic environment. [ABSTRACT FROM AUTHOR]

IntroductionGyrification is the intricate process through which the mammalian cerebral cortex develops its characteristic pattern of sulci and gyri. Monitoring gyrification provides valuable insights into brain development and identifies potential abnormalities at an early stage. This study analyzes the cortical structure in neurotypical and pathological (spina bifida) fetuses using various shape descriptors to shed light on the gyrification process during pregnancy.MethodsWe compare morphometric properties encoded by commonly used scalar point-wise curvature-based signatures—such as mean curvature (H), Gaussian curvature (K), shape index (SI), and curvedness (C)—with multidimensional point-wise shape signatures, including spectral geometry processing methods like the Heat Kernel Signature (HKS) and Wave Kernel Signature (WKS), as well as the Signature of Histograms of Orientations (SHOT), which combines histogram and signature techniques. These latter signatures originate from computer graphics techniques and are rarely applied in the medical field. We propose a novel technique to derive a global descriptor from a given point-wise signature, obtaining GHKS, GWKS, and GSHOT. The extracted signatures are then evaluated using Support Vector Regression (SVR)-based algorithms to predict fetal gestational age (GA).ResultsGSHOT better encodes the GA to other global multidimensional point-wise shape signatures (GHKS, GWKS) and commonly used scalar point-wise curvature-based signatures (C, H, K, SI, FI), achieving a prediction R2 of 0.89 and a mean absolute error of 6 days in neurotypical fetuses, and a R2 of 0.64 and a mean absolute error of 10 days in pathological fetuses.ConclusionGSHOT provides researchers with an advanced tool to capture more nuanced aspects of fetal brain development and, specifically, of the gyrification process.

Automated technologies are increasingly widespread in health care and are used to diagnose crucial disorders such as cancer cells. A skin lesion is a kind of skin cancer with benign and malignant components, and early detection is becoming a typical need. Many researchers have established these types of techniques in the past, yet the need for an efficient method still exists to improve the performance of the skin cancer detection process. Deep learning technology is chosen in this research to detect skin lesions from the provided samples. An improved LeNET method is trained with a feature set optimized using the cuckoo search technique. Here, the feature-based deep learning model presents the novelty of the technique designed with various hybrid shape descriptors and compares their performance. Based on accuracy, a feature-based Convolution Neural Network (CNN) with hybrid SURF and ORB has the highest accuracy of 99.62% for skin lesion detection compared to other distinct combinations used in this work. The findings illustrate the usefulness of several hybrid features and their performance with a deep learning model for skin lesion detection. [ABSTRACT FROM AUTHOR]

Although there is a wide range of shape descriptors available in the literature, most of them are restricted to a specific class of shapes and no one can achieve satisfactory shape retrieval results when used with different classes of shapes. Introducing new descriptors, improving, or merging existing descriptors are potential strategies for enhancing shape retrieval algorithms. In this paper, we propose a Query Perturbation-based (QPert) method for shape retrieval. QPert perturbs the query shape to create copies or clones that are closer than the query itself to the database shapes. Clones are created by adding a small noise to the coordinates of a randomly selected subset of mesh vertices or applying genetic operators between existing clones. A Genetic Algorithm (GA) gradually develops a population of clones so that the fittest clones get closer and closer to their most similar shapes in the database. The GA is implemented as a multiagent system (MAS) that enables any number of shape descriptors, classical or modern, to cooperate without the need for synchronization or direct communication between agents. Experimental results and comparisons demonstrate the advantages of this approach, regardless of the shape descriptors used. [ABSTRACT FROM AUTHOR]

In this study, malarial patients (n = 80) were treated for three days with artesunate and amodiaquine (ASAQ). Twenty healthy individuals in a similar age range were chosen as the control group. The contours of the erythrocytes were extracted from the processed microscopic images, and the shape parameters were derived from them. The erythrocytes were scanned along their largest diameter to determine the variation in grey-level intensity. The results showed that the change in shape parameters and grey-level intensity was similar to normal on day 4 after treatment. This provides novel insights into the contribution of ASAQ to the recovery of erythrocytes. [ABSTRACT FROM AUTHOR]

Since, in many routine analytical laboratories, a stereomicroscope coupled with a digital camera is not equipped with advanced software enabling automatic detection of features of observed objects, in the present study, a procedure of feature detection using open-source software was proposed and validated. Within the framework of applying microscopic expertise coupled with image analysis, a set of digital images of microplastic (MP) items identified in organs of fish was used to determine shape descriptors (such as length, width, item area, etc.). The edge points required to compute shape characteristics were set manually in digital images acquired by the camera coupled with a binocular, and respective values were computed via the use of built-in MotiConnect software. As an alternative, a new approach consisting of digital image thresholding, binarization, the use of connected-component labeling, and the computation of shape descriptors on a pixel level via using the functions available in an OpenCV library or self-written in C++ was proposed. Overall, 74.4% of the images were suitable for thresholding without any additional pretreatment. A significant correlation was obtained between the shape descriptors computed by the software and computed using the proposed approach. The range of correlation coefficients at a very high level of significance, according to the pair of correlated measures, was higher than 0.69. The length of fibers can be satisfactorily approximated using a value of half the length of the outer perimeter (r higher than 0.75). Compactness and circularity significantly differ for particles and fibers. [ABSTRACT FROM AUTHOR]

The prevalence of microplastics (MP) (< 5 mm) in aquatic habitats has recently raised concerns owing to their influence on humans and aquatic organisms, as they absorb organic pollutants and pathogens from the surrounding media because of their higher surface-to-volume ratio. Freshwater systems are severely affected by the increased intake of discarded waste from diverse sources. This study focused on the microplastic-to-zooplankton ratio and its potential impact on the environment's food chain. The sampling sites of Kolavai Lake were divided into three zones (18 stations) to investigate the spatial distribution of microplastics and zooplankton biota. The average microplastic abundance was 6.1 ± 2.5 particles/L. Fourier transform infrared spectroscopy (FTIR) and SEM analysis were performed to understand the chemical composition and surface morphology of microplastics. Water samples collected along the Central and Southern Zones revealed a high abundance of microplastics, which might be due to anthropogenic activities. A negative correlation was observed between the abundance of microplastics and zooplankton. The microplastic-to-zooplankton ratio was found to range from 0.05 to 0.74. Furthermore, the impact of microplastics in the lake ecosystem was analysed using the size and shape descriptors for both zooplankton and microplastics. These findings suggest that microplastics built up in aquatic environments, particularly those with rich biota, could be a severe concern because of their capacity to infiltrate the food web. [ABSTRACT FROM AUTHOR]

Microplastics have been detected in large numbers around the world. Not only their sheer number threatens ecosystems, their biodiversity, and human health, but risks are also posed by particle characteristics such as size and shape. However, at the moment their measurement is neither comprehensive nor harmonized, making the data ineligible for risk assessment. To change this, we propose an image-based workflow, whose six steps are oriented to international guidelines and lessons learned from more developed research fields. Best practices for sample preparation, image acquisition, and digital image processing are reviewed to assure accurate and unbiased particle measurements. On behalf of this, we selected metrics to quantitatively characterize both size and shape. The size of microplastics should be estimated via the maximum Feret's diameter. Particle shape can be measured via shape descriptors, for which we derive harmonized formulas and interpretation. Roundness, solidity, and elongation were selected by applying hierarchical agglomerative clustering and correlation analysis. With these three shape descriptors, all currently charaterizable dimensions of particle shape can be measured. Finally, we present actions for quality control as well as quality assurance and give recommendations for method documentation and data reporting. By applying our practical primer, microplastic researchers should be capable of providing informative and comparable data on particle characteristics. From this improved data, we expect to see great progress in risk assessment, meta-analyses, theory testing, and fate modeling of microplastics. [ABSTRACT FROM AUTHOR]

The morphology of granular materials, such as sands, is of significant importance due to the effect of grain shape on their physical, mechanical, and hydraulic behavior. As technology has progressed from visual identification to modern computer-based techniques, numerous methods have been developed for quantifying grain shapes, many of which utilize digital image analysis and advances in computational techniques. A comprehensive understanding of available shape characterization methods is essential to make better use of these tools. This paper presents a state-of-the-art review of current methods for characterizing the morphology of granular materials, focusing particularly on digital image analysis techniques. It critically evaluates two essential aspects of shape characterization: the acquisition of particle shape information and shape measurement methods, discussing the strengths and limitations of each approach. Further, the application of grain shape characterization to analyze the effect of particle shape on the macro-scale behavior of sand is discussed. The review emphasizes the need to shift from classical shape characterizations developed by sedimentologists to objective-oriented shape characterizations that enable micro-to-macro correlations, taking into account the availability of robust tools and technologies. [ABSTRACT FROM AUTHOR]

Abstract Background Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to translate the ROI to analyzable shape representations and features is greatly needed. Results We developed SAFARI (shape analysis for AI-segmented images), an open-source R package with a user-friendly online tool kit for ROI labelling and shape feature extraction of segmented maps, provided by AI-algorithms or manual segmentation. We demonstrated that half of the shape features extracted by SAFARI were significantly associated with survival outcomes in a case study on 143 consecutive patients with stage I–IV lung cancer and another case study on 61 glioblastoma patients. Conclusions SAFARI is an efficient and easy-to-use toolkit for segmenting and analyzing ROI in medical images. It can be downloaded from the comprehensive R archive network (CRAN) and accessed at https://lce.biohpc.swmed.edu/safari/ .

The advancement in technology also upgraded the biomedical field and enhanced the diagnostic system performance, where medical imaging plays its inevitable role. Several image processing techniques created the medical systems automated and meticulous. These systems nowadays support physicians in identifying adverse diseases like cancer, tumor, and skin diseases. In this imaging, features have a significant role in visually performing the diagnostic and finding the suffered area. Moreover, it also improves the performance of the automatic recognition systems used in the medical field. The researchers proposed various feature extraction techniques that use color, texture, and shape-based features in image processing systems but not every feature is best suited for medical imaging. This paper covers the objective of providing a Fast and Indented Feature Extractor (FIFE) for the health care sector's future. This proposed extractor extracts only relevant shape features for processing to reduce processing time and improve the result's quality. The image samples are taken for a skin disease named skin lesions for this work. The dataset of ISIC and PH2 is used in this work for analysis. This FIFE works on multiple feature extraction approaches and blends them to form a distinct extraction level. The indentation level one, two, three, and four are used in this work using four different shape descriptors, namely SURF, FAST, BRISK, and ORB. The performance comparison of the proposed indentation levels is based on the number of features and time. The experimentation results are evaluated with qualitative and quantitative measures that reveal the proposed extractor's efficiency. [ABSTRACT FROM AUTHOR]

Microglia are heterogenous cells characterized by distinct populations each contributing to specific biological processes in the nervous system, including neuroprotection. To elucidate the impact of sex-specific microglia heterogenicity to the susceptibility of neuronal stress, we video-recorded with time-lapse microscopy the changes in shape and motility occurring in primary cells derived from mice of both sexes in response to pro-inflammatory or neurotoxic stimulations. With this morpho-functional analysis, we documented distinct microglia subpopulations eliciting sex-specific responses to stimulation: male microglia tended to have a more pro-inflammatory phenotype, while female microglia showed increased sensitivity to conduritol-B-epoxide (CBE), a small molecule inhibitor of glucocerebrosidase, the enzyme encoded by the GBA1 gene, mutations of which are the major risk factor for Parkinson's Disease (PD). Interestingly, glucocerebrosidase inhibition particularly impaired the ability of female microglia to enhance the Nrf2-dependent detoxification pathway in neurons, attenuating the sex differences observed in this neuroprotective function. This finding is consistent with the clinical impact of GBA1 mutations, in which the 1.5–2-fold reduced risk of developing idiopathic PD observed in female individuals is lost in the GBA1 carrier population, thus suggesting a sex-specific role for microglia in the etiopathogenesis of PD-GBA1. [ABSTRACT FROM AUTHOR]

3D non-rigid shape correspondence, as an important research topic in 3D shape analysis, is useful but challenging in computer graphics, computer vision, and pattern recognition. Despite recent success of several deep neural networks for shape correspondence, those networks cannot achieve robust results on non-rigid objects due to their local deformation complexity. This paper presents a novel and efficient shape correspondence network—Shared Optimized Res-CapsuleNet (SORCNet)—that learns point features based on enhanced descriptors to solve dense correspondence between non-rigid 3D shapes. To further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure, based on the stochastic gradient descent algorithm with momentum and weight decay (SGDW). Moreover, as the convolutional neural network does not perform well when the shape has directional variance, we present a shared capsule network structure with dual routings, which correlates the hierarchical geometric relationships of the semantic parts well to extract more informative point features. We proved that the primary capsule has a greater influence on feature extraction than the routing and decoder parts. The entire network, SORCNet, is integrated and trained/tested by taking the descriptors and Laplacian eigenbases of two shapes as input. The experiments on public datasets, such as FAUST, SCAPE, TOSCA and KIDS, demonstrate the better effectiveness, accuracy, and adaptability of our method than those of the state of the art in 3D shape correspondence. [ABSTRACT FROM AUTHOR]

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Microplastic pollution is one of the greatest environmental concerns for contemporary times and the future. In the last years, the number of publications about microplastic contamination has increased rapidly and the list is daily updated. However, the lack of standard analytical approaches might generate data inconsistencies, reducing the comparability among different studies. The present study investigates the potential of two image processing tools (namely the shapeR package for R and ImageJ 1.52v) in providing an accurate characterization of the shape of microplastics using a restricted set of shape descriptors. To ascertain that the selected tools can measure small shape differences, we perform an experiment to verify the detection of pre-post variations in the shape of different microplastic types (i.e., nylon [NY], polyethylene [PE], polyethylene terephthalate [PET], polypropylene [PP], polystyrene [PS], and polyvinylchloride [PVC]) treated with mildly corrosive chemicals (i.e., 10% KOH at 60 °C, 30% H2O2 at 50 °C, and 15% H2O2 + 5% HNO3 at 40 °C; incubation time ≈ 12 h). Analysis of surface area variations returns results about the vulnerability of plastic polymers to digestive solutions that are aligned with most of the acquired knowledge. The largest decrease in surface area occurs for KOH-treated PET particles, while NY results in the most susceptible polymer to the 30% H2O2 treatment, followed by PVC and PS. PE and PP are the most resistant polymers to all the used treatments. The adopted methods to characterize microplastics seem reliable tools for detecting small differences in the shape and size of these particles. Then, the analytic perspectives that can be developed using such widely accessible and low-cost equipment are discussed. [ABSTRACT FROM AUTHOR]

Article highlights 1. The 3D particle geometries of typical energetic materials are scanned by X-ray CT and identified by improved watershed analysis. 2. The 2D and 3D computational geometry techniques are developed for determining seven commonly used particle shape descriptors. 3. The stereology strategy is used to obtain 2D projections of 3D particles and evaluate the effectiveness of 2D shape descriptors to infer 3D particle shapes of energetic materials.

Characterizing graphite shape in cast irons is of importance for control of cast components. This is still done based on standard charts and has been more and more supported by quantitative image analysis during the last decades. Despite the significant progresses made by image analysis techniques and software packages, the results of quantitative image analysis are still moderately satisfactory, and the question raised of the capabilities of machine learning to supplement this approach. This work first presents a review on graphite shape analysis by standard image analysis and also lists the very few works that have used machine learning approach. Then a series of challenging images in which both lamellar, compacted and spheroidal graphite coexist was submitted to standard image analysis and to a convolutional neural network, a deep learning model. The comparison of the two methods shows that machine learning produced very encouraging results when compared to standard image analysis, in particular for what concerns the presence of lamellar graphite. [Display omitted] • A holistic literature review of graphite classification and characterization was provided. • Deep Learning was compared to Quantitative Image Analysis classification. • Deep Learning distinguished Lamellar from Compact graphite more effectively. • Quantitative Image Analysis was more efficient to distinguish spheroids and irregular graphites. [ABSTRACT FROM AUTHOR]

Since, in many routine analytical laboratories, a stereomicroscope coupled with a digital camera is not equipped with advanced software enabling automatic detection of features of observed objects, in the present study, a procedure of feature detection using open-source software was proposed and validated. Within the framework of applying microscopic expertise coupled with image analysis, a set of digital images of microplastic (MP) items identified in organs of fish was used to determine shape descriptors (such as length, width, item area, etc.). The edge points required to compute shape characteristics were set manually in digital images acquired by the camera coupled with a binocular, and respective values were computed via the use of built-in MotiConnect software. As an alternative, a new approach consisting of digital image thresholding, binarization, the use of connected-component labeling, and the computation of shape descriptors on a pixel level via using the functions available in an OpenCV library or self-written in C++ was proposed. Overall, 74.4% of the images were suitable for thresholding without any additional pretreatment. A significant correlation was obtained between the shape descriptors computed by the software and computed using the proposed approach. The range of correlation coefficients at a very high level of significance, according to the pair of correlated measures, was higher than 0.69. The length of fibers can be satisfactorily approximated using a value of half the length of the outer perimeter (r higher than 0.75). Compactness and circularity significantly differ for particles and fibers.

Premise: Chenopodium oahuense is a polymorphic Hawaiian endemic plant inhabiting several xeric habitats. Considerable variability in leaf morphology has made comprehensively describing its diversity difficult. Chenopodium oahuense subsp. ilioense is differentiated from C. oahuense subsp. oahuense by smaller, less‐lobed, succulent leaves, smaller seeds, and prostrate to scandent habit. The lacking quantification of leaf shape, succulence, and previously unknown heteroblastic leaf transition in C. oahuense subsp. ilioense complicates the morphological boundaries separating subspecies. Methods: This study used landmark analyses, elliptical Fourier descriptors (EFDs), and traditional shape descriptors measured from 1585 greenhouse‐grown plant leaves collected over 18 weeks. Principal component analyses visualized correlations in leaf shape, and linear discriminant analyses predicted classifications, either subspecific or heteroblastic. Results: Identity determination and heteroblastic change visualization were limited in landmark analyses. On the basis of EFDs and shape descriptors, C. oahuense subsp. ilioense was determined to be morphologically differentiated from C. oahuense subsp. oahuense with the Pu'u Ka Pele population as intermediate. The EFDs depicted heteroblastic change, predominantly in lobing. All analyses were restricted in correctly attributing a leaf to the week collected. Shape descriptors generally represented significant heteroblastic change over the growth period. Conclusions: These analyses support significant differentiation between the subspecies, particularly from shape descriptors. Furthermore, we quantified the morphological intermediacy of the Pu'u Ka Pele population. Results suggest this population could be the result of incomplete lineage sorting or a recent hybridization of the two subspecies. Hawaiian Chenopodium is a polymorphic lineage notable for future research in adaptive radiations, phenotypic plasticity, and heteroblasty. [ABSTRACT FROM AUTHOR]

Background: Recent developments to segment and characterize the regions of interest (ROI) within medical images have led to promising shape analysis studies. However, the procedures to analyze the ROI are arbitrary and vary by study. A tool to translate the ROI to analyzable shape representations and features is greatly needed. Results: We developed SAFARI (shape analysis for AI-segmented images), an open-source R package with a user-friendly online tool kit for ROI labelling and shape feature extraction of segmented maps, provided by AI-algorithms or manual segmentation. We demonstrated that half of the shape features extracted by SAFARI were significantly associated with survival outcomes in a case study on 143 consecutive patients with stage I–IV lung cancer and another case study on 61 glioblastoma patients. Conclusions: SAFARI is an efficient and easy-to-use toolkit for segmenting and analyzing ROI in medical images. It can be downloaded from the comprehensive R archive network (CRAN) and accessed at https://lce.biohpc.swmed.edu/safari/. [ABSTRACT FROM AUTHOR]

This paper proposes a feature characterization method that can discriminate between man-made and natural objects on the seabed using a pair of SAS and optical images. Feature matching methods, like scale-invariant feature transform (SIFT), usually utilize gradient information to detect feature points, whereas contour-based features often use geometrical descriptors such as roughness, circularity, and solidity. However, due to the different characteristics of sonar and optical imagery, current descriptors cannot be directly applied over sonar-optical image pairs. The clarity of the water (muddy water, concentration of particles that scatter light) for optical images, and the stability of the platform, and reflections from seabed for SAS images are important parameters that distort the two images and make it hard to match between the two image sources. This paper proposes a feature characterization method that can identify man-made objects in underwater optic-SAS image pairs. Two new contour-based feature descriptors are introduced. The first is the entropy angle feature, which aims to reflect the entropy of the angles that originate from points on the contour. The second feature is local curve fitting, which characterizes the radial-polar space of the local curve polynomial fitting. The descriptors are designed to characterize man-made objects whose contour is expected to be smoother than natural objects such as rocks. Experimental results on a real, large dataset comprised of 1,519 optic-SAS image pairs, are shown to verify that the proposed method has a high level of classification accuracy and good discrimination between man-made and natural objects. [ABSTRACT FROM AUTHOR]

In order to achieve quality control of nickel-based superalloy powders, it is necessary to quantitatively characterize the particle shape. This paper introduces how the image processing techniques were used to quantitatively evaluate the particle shape and its distribution. Firstly, the appropriate edge extraction operator was determined by multiple comparisons of processing effect of six edge extraction operators. It was found that the morphological processing technology, being similar to the watershed algorithm, demonstrated strong applicability for edge extraction. Secondly, eight shape descriptors were calculated and verified by standard graphics to ensure the accuracy of image processing algorithms. By monitoring single particle, it was found that the image processing algorithms can accurately distinguish the spherical particle from the satellite particle, and the shape descriptors change in an obvious manner. Finally, the probability density distributions of eight shape descriptors were demonstrated and compared. The results show that the image processing techniques can effectively characterize the particle shape and its distribution, which provides a methodological basis for the characterization of superalloy powders and theoretical guidance for the adjustment of atomization process. By mathematically fitting, a relationship was found between the mean shape descriptors and the angle of repose (AOR). Therefore, the method can be used to obtain the average value of shape descriptors, and then establish the relationship between the average shape descriptors and powder flowability, which can predict the process performance of powder and the performance during 3D printing. [Display omitted] • We studied the image algorithms for shape characterization of superalloy powders. • The particle shape and its distribution can be identified by the image processing. • This provides a methodological basis for quantitative powder characterization. • We establish relationship between mean shape descriptors and powder flowability. • The relationship can predict the process performance of powder during 3D printing. [ABSTRACT FROM AUTHOR]

The metal screws used in osteosynthesis are fabricated with precise dimensions in order to prevent complications in the surgical procedure, and to optimize costs and production time. This research work proposes a control system consisting of a USB digital microscope, a LED lighting base, and a graphical user interface (GUI) developed in ImageJ (open-source software), the main objective in this work is validate metrologically the screws dimensions quality. In the results, the screws length and width, obtained with the proposed system, and the same measurement obtained with a caliper were compared as a metrological validation procedure. [ABSTRACT FROM AUTHOR]

The Structure Light System (SLS) is a general concept and it is one of the cheapest methods for the non-contact-based 3D reconstruction. The existing single-shot SLS which is primarily based on the spatial encoding techniques are not optimal in terms of resolution and digitally encoded patterns. Those schemes are not flexible, controllable, and designed up to the level of the pixel. So, to increase the resolution and to implement a flexible controllable pattern we proposed a novel heuristic method based on the spatial neighborhood. In this paper, we propose a multi-resolution SLS which can be implemented with a set of 25 geometrical shaped distinct symbols or alphabets to use in the projection pattern as shape primitive. The size of each symbol is well defined in pixels which enabled us to have access and control up to the full resolution of the projector. The shape descriptive parameters for each symbol or alphabet are also defined and computed. To spread the alphabets in a controllable manner, a method is defined to generate a robust pseudo-random sequence of any required size with a certain number of alphanumeric bases, to be employed in the projection pattern concerning the measured resolution. This arrangement will enable us to design the projection patterns according to the required surface area and the resolution. A new technique is developed for the decoding of the captured image pattern. The decoding process depends upon the classification of symbols which is based on shape descriptive parameters. The searching in the neighborhood of a symbol is carried out through computing the location information, grid distance, and direction information to find the codewords which are used to establish the correspondence.

Using mineral admixtures has been proved to be significant for the production of cement-based composites. Burnt Clay Powder (BCP), Rice Husk Ash (RHA), Coconut Shell Ash (CSA) and Gum Arabic (GA) can be used as cement substitutes in cement-based composites whilst Cassava Starch (CS) can be used as an admixture in cement-based composites. Understanding shape parameters, microstructure and morphology of mineral admixtures and thus utilising them more efficiently for practical applications is critically required. Although research has been conducted to characterise mineral admixtures, limited studies have documented the combined Scanning Electron Microscopy (SEM) and image analysis. Thanks to image analysis-based methodology, it was possible to establish circularity (Circ), roundness (RD), solidity (SLD), axial ratio (AR), surface porosity (SP) and surface roughness (SR) of pozzolans and admixtures. This involved usage of SEM images of mineral admixtures to establish shape descriptors, porosity and surface roughness of mineral admixtures using image-based analysis and assessing the correlation and interdependence among the parameters. The mineral admixtures in this study were characterised by average values of solidity, roundness, circularity and axial ratio of greater than 0.7. Evaluation of correlation between roundness and circularity illustrated weak R2 values ranging from 0.30 to 0.40 for all mineral admixtures. The axial ratio also weakly correlated with solidity with a wider range of R2 values from 0.27 to 0.45. Importantly, the relationships were noticed to be significant in the interpretation of morphology and shape data.

Microglia are heterogenous cells characterized by distinct populations each contributing to specific biological processes in the nervous system, including neuroprotection. To elucidate the impact of sex-specific microglia heterogenicity to the susceptibility of neuronal stress, we video-recorded with time-lapse microscopy the changes in shape and motility occurring in primary cells derived from mice of both sexes in response to pro-inflammatory or neurotoxic stimulations. With this morpho-functional analysis, we documented distinct microglia subpopulations eliciting sex-specific responses to stimulation: male microglia tended to have a more pro-inflammatory phenotype, while female microglia showed increased sensitivity to conduritol-B-epoxide (CBE), a small molecule inhibitor of glucocerebrosidase, the enzyme encoded by the GBA1 gene, mutations of which are the major risk factor for Parkinson’s Disease (PD). Interestingly, glucocerebrosidase inhibition particularly impaired the ability of female microglia to enhance the Nrf2-dependent detoxification pathway in neurons, attenuating the sex differences observed in this neuroprotective function. This finding is consistent with the clinical impact of GBA1 mutations, in which the 1.5–2-fold reduced risk of developing idiopathic PD observed in female individuals is lost in the GBA1 carrier population, thus suggesting a sex-specific role for microglia in the etiopathogenesis of PD-GBA1.

Three-dimensional digitization technologies have been proved as reliable methods for detailed and accurate spatial data collection from existing cultural heritage. In addition, the point segmentation techniques are particularly relevant for contour detection and classification of the unstructured point cloud. This paper describes an approach to obtain 2D CAD-like visualizations of complex geometry from photogrammetric models so that the detected contours of particular object elements can be used for 2D shape analysis. The work process uses the point clouds derived from photogrammetric models to create the plane visualization of the object's geometry by segmenting points based on the verticality geometric feature. The research presented is on the case studies of iconostases as the specific art and architectural elements of the Christian Orthodox church that can be appreciated only in situ. To determine relations between the characteristics of the particular shapes and the iconostases' style origins, the mathematical method of shape analysis was applied. This study aims to numerically describe the stylistic characteristics of the shapes of the main parts of the iconostasis concerning the artistic period to which it belongs to. The concept was based on the consideration of global shape descriptors and associated shape measurements which were used to analyze and classify the stylistic characteristics of the iconostases. The methodology was applied to the representative examples of three iconostases from the Baroque and Classicism art movements. The results illustrated that the proposed methods and techniques, with certain improvements, could be helpful for CAD visualization and shape analysis of complex geometry. [ABSTRACT FROM AUTHOR]

Little is known about possible differences in sagitta otolith size and shape between sexes of the shi drum, Umbrina cirrosa, and relationships between their body and otolith size. Thus, this study aimed to fill this knowledge gap via examination of 414 sagittal otoliths from 108 male (total length 13.8–26.8 cm) and 99 female (13.5–26.7 cm) U. cirrosa caught between May 2017 and April 2018 in gillnets set at a depth of ~15 m in Mersin Bay, Eastern Mediterranean Sea. No statistical differences were observed between the shape indices of the left‐sided and right‐sided sagitta. However, there were significant differences in the size and shape of otoliths between males and females. The slopes of allometric power functions from otolith width × fish sizes gave significant differences between males and females (ANCOVA, P < 0.05). The relationship for length × weight of otoliths from both males and females showed isometric growth, whereas the relationship of otolith width × otolith weight showed positive allometry. Negative allometric growth was observed for the relationship otolith length × otolith width. In summary, this study revealed the presence of sexual dimorphism in the otolith shape of U. cirrosa, and the data on regression relationships of fish‐otolith sizes can be used to estimate fish size from U. cirrosa otolith sizes. [ABSTRACT FROM AUTHOR]

Shape descriptors provide valuable prior information in many tomographic image reconstruction methods. Such descriptors include, among others, centroid, circularity, orientation, and elongation. Shape descriptor measures are often analytically expressed as a composition of certain geometric moments. Building upon this fact, this paper suggests preserving the values of a specific geometric moment in the reconstruction process, instead of preserving entire descriptors, as it has been suggested so far. Reconstructions from two natural projection directions (vertical and horizontal) are considered with special attention. The provided theoretical analysis demonstrates that preserving the value of a specific geometric moment, provided as prior information for the reconstruction process, simultaneously ensures the preservation of the true measures of all four abovementioned descriptors. Based on this result, a novel regularized energy minimization reconstruction model is proposed. The minimization task of the new model is solved using gradient-based optimization algorithm. Performance evaluation of the proposed method is supported by experimental results obtained through comparisons with other well-known reconstruction methods. • The paper introduces a new geometrical moment preserving based tomographic reconstruction model. • The preserved moment implies preservation of following descriptors: orientation, elongation, centroid and circularity. • Analytical gradient of the proposed energy functions is derived, which makes the minimization process more efficient. [ABSTRACT FROM AUTHOR]

A “landscape dominant element” – an object with the greatest range of visual impact on the surrounding space, of a strong form that integrates the entirety of a composition, distinguished by its height, dimensions, colour, material, texture or the variety of its details. The attempts to define the concept presented herein, and which is intuitively perceived as obvious, illustrate its ambiguity. They bring to mind a visual contrast between this subject and others that surround it. This article attempts to analyse views using the author’s computer program. The objects in the photos are characterized by their interference in a panorama silhouette or skyline, size, colour, height, and shape. This helped to identify those that clearly stand out from the other forms, with which they come into visual interaction. The purpose of these considerations is to create tools that allow for a partial objectification of the landscape composition assessment.

Computer-aided diagnosis (CAD) of heart diseases using machine learning techniques has recently received much attention. In this study, we present a novel parametric-based feature selection method using the three-dimensional spherical harmonic (SHs) shape descriptors of the left ventricle (LV) for intelligent myocardial infarction (MI) classification. The main hypothesis is that the SH coefficients of the parameterized endocardial shapes in MI patients are recognizable and distinguishable from healthy subjects. The SH parameterization, expansion, and registration of the LV endocardial shapes were performed, then parametric-based features were extracted. The proposed method performance was investigated by varying considered phases (i.e., the end-systole (ES) or the end-diastole (ED) frames), the spatial alignment procedures based on three modes (i.e., the center of the apical (CoA), the center of mass (CoM), and the center of the basal (CoB)), and considered orders of SH coefficients. After applying principal component analysis (PCA) on the feature vectors, support vector machine (SVM), K-nearest neighbors (K-NN), and random forest (RF) were trained and tested using the leave-one-out cross-validation (LOOCV). The proposed method validation was performed via a dataset containing healthy and MI subjects selected from the automated cardiac diagnosis challenge (ACDC) database. The promising results show the effectiveness of the proposed classification model. SVM reached the best performance with accuracy, sensitivity, specificity, and F-score of 97.50%, 95.00%, 100.00%, and 97.56%, respectively, using the introduced optimum feature set. This study demonstrates the robustness of combining the SH coefficients and machine learning techniques. We also quantify and notably highlight the contribution of different parameters in the classification and finally introduce an optimal feature set with maximum discriminant strength for the MI classification task. Moreover, the obtained results confirm that the proposed method performs more accurately than conventional point-based methods and also the current start-of-the-art, i.e., clinical measures. We showed our method's generalizability using employing it in dilated cardiomyopathy (DCM) detection and achieving promising results too. Parametric-based feature selection via spherical harmonics coefficients for the left ventricle myocardial infarction screening. [ABSTRACT FROM AUTHOR]