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Due to the complexity of open-channel networks in which a lengthy procedure is required to understand and analyze their behavior. This paper introduces a methodology for the skeletonization of complex tree-shaped open channel systems. The aim is to aggregate separate branches into one equivalent branch, which has a new skeletonization factor. Three different illustrative examples of river systems using two approaches are analyzed. The software package HEC-RAS is employed to simulate the proposed open channel networks as a one-dimensional steady, gradually varied flow state. Results show that there is a strong correlation between the skeletonization factor required for the equivalent branch and the flow rate of the equivalent branch. The skeletonization factor is also significantly affected by an increase in the ratio of the bed width of each branch canal to that of the main channel in Approach A. The water depth along the main channel has increased more significantly in approach B than in approach A, with average increases of 49.5 %, 29.2 %, and 14.1 % for a number of branches equal to 2, 4, and 6, respectively. Finally, the developed skeletonization concept can be used to represent complex open-channels network models.

Due to the complexity of open-channel networks in which a lengthy procedure is required to understand and analyze their behavior. This paper introduces a methodology for the skeletonization of complex tree-shaped open channel systems. The aim is to aggregate separate branches into one equivalent branch, which has a new skeletonization factor. Three different illustrative examples of river systems using two approaches are analyzed. The software package HEC-RAS is employed to simulate the proposed open channel networks as a one-dimensional steady, gradually varied flow state. Results show that there is a strong correlation between the skeletonization factor required for the equivalent branch and the flow rate of the equivalent branch. The skeletonization factor is also significantly affected by an increase in the ratio of the bed width of each branch canal to that of the main channel in Approach A. The water depth along the main channel has increased more significantly in approach B than in approach A, with average increases of 49.5 %, 29.2 %, and 14.1 % for a number of branches equal to 2, 4, and 6, respectively. Finally, the developed skeletonization concept can be used to represent complex open-channels network models. [ABSTRACT FROM AUTHOR]

The pore geometry and topology properties of rocks are significant for a better understanding of the complex hydrologic and elastic properties. If the complexity of porous media is considered, reliable results can achieve. In three-dimensional imaging of cavities, these complexities are taken into account. In this study, by conducting different methods deep understanding from some sandstones pore space is undertaken. Three series of 2D microcomputed tomography sandstones binary images have been considered, each of them imagined as a 3D binary image. Using novel skeletonization and pore-throat partitioning algorithms some network properties have been evaluated and compared for three cases. Those properties are pore and grain size distribution, throat length frequency, and coordination number frequency. Also, geometric measures in 2D and 3D have been considered using Minkowski functionals. The area, the perimeter and the 2D Euler number of 2D binary images and the volume, the surface area, the mean breadth also known as integral of the mean curvature, and the 3D Euler Number of 3D binary images are considered. [ABSTRACT FROM AUTHOR]

Yoga is a years discipline that calls for physical postures, mental focus, and deep breathing. Yoga practice can enhance stamina, power, serenity, flexibility, and well-being. Yoga is currently a well-liked type of exercise worldwide. The foundation of yoga is good posture. Even though yoga offers many health advantages, poor posture can lead to issues including muscle sprains and pains. People have become more interested in working online than in person during the last few years. People who are accustomed to internet life and find it difficult to find the time to visit yoga studios benefit from our strategy. Using the web cameras in our system, the model categorizes the yoga poses, and the image is used as input. However, the media pipe library first skeletonizes that image. Utilizing a variety of deep learning models, the input obtained from the yoga postures is improved to improve the asana. The algorithms like VGG16 (Visual Geometric Group), VGG19, Convo2d, CNN. [ABSTRACT FROM AUTHOR]

Smoothing filters are widely used in EEG signal processing for noise removal while preserving signals’ features. Inspired by our recent work on Upscale and Downscale Representation (UDR), this paper proposes a cascade arrangement of some effective image-processing techniques for signal filtering in the image domain. The UDR concept is to visualize EEG signals at an appropriate line width and convert it to a binary image. The smoothing process is then conducted by skeletonizing the signal object to a unit width and projecting it back to the time domain. Two successive UDRs could result in a better-smoothing performance, but their binary image conversion should be restricted. The process is computationally ineffective, especially at higher line width values. Cascaded Thinning UDR (CTUDR) is proposed, exploiting morphological operations to perform a two-stage upscale and downscale within one binary image representation. CTUDR is verified on a signal smoothing and classification task and compared with conventional techniques, such as the Moving Average, the Binomial, the Median, and the Savitzky Golay filters. Simulated EEG data with added white Gaussian noise is employed in the former, while cognitive conflict data obtained from a 3D object selection task is utilized in the latter. CTUDR outperforms its counterparts, scoring the best fitting error and correlation coefficient in signal smoothing while achieving the highest gain in Accuracy (0.7640%) and F-measure (0.7607%) when used as a smoothing filter for training data of EEGNet.

In this comprehensive study, a novel MATLAB to Python (M-to-PY) conversion process is showcased, specifically tailored for an intricate image skeletonization project involving fifteen MATLAB files and a large dataset. The central innovation of this research is the adept use of ChatGPT-4 as an AI assistant, pivotal in crafting a prototype M-to-PY converter. This converter's capabilities were thoroughly evaluated using a set of test cases generated by the Bard bot, ensuring a robust and effective tool. The culmination of this effort was the development of the Skeleton App, adept at image sketching and skeletonization. This live and publicly available app underscores the enormous potential of AI in enhancing the transition of scientific research from MATLAB to Python. The study highlights the blend of AI's computational prowess and human ingenuity in computational research, making significant strides in AI-assisted scientific exploration and tool development. [ABSTRACT FROM AUTHOR]

Yoga is a centuries-old style of exercise followed by sports personnel, patients, and physiotherapist as their regime. A correct posture and technique are the key points in yoga to reap the maximum benefits. Hence, developing a model to classify yoga postures correctly is a recently emerging research topic. The paper presents a novel architecture that aims to classify various yoga poses. The proposed model estimates and classifies yoga poses into five broad categories with low latency. In the proposed architecture, the images are skeletonized before inputting into the model. The skeletonization process is done using the MediaPipe library for body keypoint detection. The paper compares the performance of various deep learning models with and without skeletonization. Different learning models showed the optimum result with the training of skeletonized images to the network. The comparison is drawn to establish the positive impact of skeletonization on the results obtained by various models. VGG16 achieves the highest validation accuracy on non-skeletonized images (95.6%), followed by InceptionV3, NASNetMobile, YogaConvo2d (proposed model) (89.9%), and lastly, InceptionResNetV2. In contrast, the proposed model YogaConvo2d using skeletonized images reports a validation accuracy of 99.62%, followed by VGG16, InceptionResNetV2, NASNetMobile, and InceptionV3. [ABSTRACT FROM AUTHOR]

Abstract Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.

Improving mechanical topology optimization (TO) results by substituting biomimetic beams is one possibility to achieve designs of mechanical components that are highly sustainable and show good mechanical performance. Because of their geometric complexity, such designs were found to be well-suited for production by laser additive manufacturing. One obstacle of incorporating biomimetics beams in TO designs is the lack of detailed design methodologies. Röver et al. ["Methodology for integrating biomimetic beams in abstracted topology optimization results," in Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition. Volume 4: Biomedical and Biotechnology; Design, Systems, and Complexity Columbus, OH, 30 October–3 November (ASME, New York, 2022)] proposed a corresponding design concept. Building on their concept, we present in this work a detailed methodology for abstraction of TO results to a design consisting of ball nodes and cylindrical beams. Using such an auxiliary design, the internal forces and moments of the beams can be evaluated to allow for the substitution of suitable biomimetic beams to generate biomimetic component designs in a next step. We present a skeletonization algorithm based on the potential field approach. Using the skeletonization and an additional analysis of the dimensions of the beams in the TO result, the algorithm develops an auxiliary design of the original TO result. The final algorithm was applied to three common TO results to obtain one auxiliary component design each. The developed algorithm was found to generate abstractions that were well-suited for use in the methodology proposed in Röver et al. ["Methodology for integrating biomimetic beams in abstracted topology optimization results," in Proceedings of the ASME 2022 International Mechanical Engineering Congress and Exposition. Volume 4: Biomedical and Biotechnology; Design, Systems, and Complexity Columbus, OH, 30 October–3 November (ASME, New York, 2022)], because internal forces and moments in the abstracted beams could be evaluated with less effort. Therefore, our work contributes to a detailed design methodology for biomimetic mechanical components in the field of design for additive manufacturing. [ABSTRACT FROM AUTHOR]

Purpose: There is controversy regarding which internal thoracic artery (ITA) should be anastomosed to the left anterior descending artery (LAD). Here, we propose an optimal graft design based on measurement of blood flow in the ITA. Methods: Sixty-one patients (53 men, median age 68 [62–75] years) undergoing first elective coronary artery bypass grafting were enrolled. Fifty-seven left ITAs (LITAs) and 28 right ITAs (RITAs) were harvested in either a semi-skeletonized manner using a harmonic scalpel covered with papaverine-soaked gauze (group-A, n = 45) or a fully skeletonized manner using electrocautery with intraluminal papaverine injection (group-B, n = 41). Free flow of 33 ITAs was measured after pharmacological dilatation and in situ ITA-LAD flow was measured in 59 patients by transit-time flowmetry. Results: RITA and LITA free flow were 147.0 [87.8–213.0] mL/min and 108.0 [90.0–144.0] mL/min, respectively (P = 0.199). The group-B had significantly higher ITA free flow (135.0 [102.0–171.0] mL/min) than group-A (63.0 [36.0–96.0] mL/min, P = 0.009). In 13 patients with bilateral ITA harvesting, free flow of the RITA (138.0 [79.5–204.0] mL/min) was also significantly higher than the LITA (102.0 [81.0–138.0] mL/min, P = 0.046). There was no significant difference between RITA and LITA flow anastomosed to the LAD. The group-B had significantly higher ITA-LAD flow (56.5 [32.3–73.6] mL/min) than group-A (40.9 [20.1–53.7] mL/min, P = 0.023). Conclusion: RITA provides significantly higher free flow than LITA but similar blood flow to the LAD. Full skeletonization with intraluminal papaverine injection maximizes both free flow and ITA-LAD flow. [ABSTRACT FROM AUTHOR]

The escalating global embrace of yoga as a holistic approach to well-being has accentuated the demand for refined and efficient techniques in yoga posture recognition. Traditional manual methods, although valuable, have exhibited protracted timelines and vulnerability to inaccuracies. In response, we introduce an innovative solution that harnesses the capabilities of deep learning (DL) models, elevating both the precision and accuracy of posture detection. Our approach predominantly leverages the Thunder variant of the MoveNet model, renowned for its exceptional proficiency in distinguishing an array of yoga postures. This model is seamlessly amalgamated with the MediaPipe technique, facilitating adept keypoint identification and skeletonization. In our proposed framework, input images undergo initial preprocessing, followed by skeletonization achieved through keypoint extraction. This pivotal process enables the encapsulation of distinctive points intrinsic to individual yoga poses. Central to our methodology is the incorporation of the large and diverse yoga (LDY) dataset, which encompasses five distinct yoga pose categories: Downdog, Goddess, Plank, Tree, and Warrior. A thorough evaluation demonstrates our approach's outstanding accuracy of 99.50% when deployed on the LDY dataset. As maintaining precise posture is pivotal in averting immediate discomfort and mitigating long-term health complexities, the implications of this advancement are profound. It charts a course toward more meticulous and accessible mechanisms for detecting yoga poses, thus profoundly influencing the physical and mental well-being of practitioners. [ABSTRACT FROM AUTHOR]

The tortuosity of retinal blood vessels is an important phenomenon, and it can act as a biomarker in the diagnosis of several eye diseases. The study of abnormalities in the tortuosity of retinal arteries and veins provides ophthalmologists with important information for disease diagnosis. Our study aims to compare the tortuosity relation between retinal arteries and veins by quantifying the vessels' tortuosity in the retina using 14 tortuosity measures applied to the AV-classification retinal dataset. Two feature sets are created, one for arteries and the other for veins. The comparison between the tortuosity of arteries and veins is based on a two-sample T-test statistical method, a regression analysis between the quantified tortuosity features, principal component analysis at the dataset level, and the introduction of the arteriovenous length ratios concept to compare the variations in these new ratios to see the tortuosity behavior in each image. The methods' results have shown that the tortuosity of retinal arteries and veins is similar. The result of the two-sample T-test supports the research hypothesis, as the P-value obtained was greater than 0.05. Furthermore, the regression analysis between arteries and veins features showed a high correlation ( r 2 = 89.39% and 89.11%) for arteries and veins, respectively. The study concludes that the retinal vessel type has no statistical significance in the tortuosity calculation results. [ABSTRACT FROM AUTHOR]

Retinal vessel segmentation, skeletonization, and the generation of vessel segments are considered significant steps in any automated system for measuring the vessel biomarkers of several disease diagnoses. Most of the current tortuosity quantification methods rely on precise vascular segmentation and skeletonization of the retinal vessels. Additionally, the existence of a reference dataset for accurate vessel segment images is an essential need for implementing deep learning solutions and an automated system for measuring the vessel biomarkers of several disease diagnoses, especially for optimized quantification of vessel tortuosity or accurate measurement of AV-nicking. This study aimed to present an improved method for skeletonizing and extracting the retinal vessel segments from the 504 images in the AV classification dataset. The study utilized the Six Sigma process capability index, sigma level, and yield to measure the vessels' tortuosity calculation improvement before and after optimizing the extracted vessels. As a result, the study showed that the sigma level for the vessel segment optimization improved from 2.7 to 4.39, the confirming yield improved from 88 percent to 99.77 percent, and the optimized vessel segments of the AV classification dataset retinal images are available in monochrome and colored formats. [ABSTRACT FROM AUTHOR]

The phenotyping of Pinus massoniana seedlings is essential for breeding, vegetation protection, resource investigation, and so on. Few reports regarding estimating phenotypic parameters accurately in the seeding stage of Pinus massoniana plants using 3D point clouds exist. In this study, seedlings with heights of approximately 15-30 cm were taken as the research object, and an improved approach was proposed to automatically calculate five key parameters. The key procedure of our proposed method includes point cloud preprocessing, stem and leaf segmentation, and morphological trait extraction steps. In the skeletonization step, the cloud points were sliced in vertical and horizontal directions, gray value clustering was performed, the centroid of the slice was regarded as the skeleton point, and the alternative skeleton point of the main stem was determined by the DAG single source shortest path algorithm. Then, the skeleton points of the canopy in the alternative skeleton point were removed, and the skeleton point of the main stem was obtained. Last, the main stem skeleton point after linear interpolation was restored, while stem and leaf segmentation was achieved. Because of the leaf morphological characteristics of Pinus massoniana, its leaves are large and dense. Even using a high-precision industrial digital readout, it is impossible to obtain a 3D model of Pinus massoniana leaves. In this study, an improved algorithm based on density and projection is proposed to estimate the relevant parameters of Pinus massoniana leaves. Finally, five important phenotypic parameters, namely plant height, stem diameter, main stem length, regional leaf length, and total leaf number, are obtained from the skeleton and the point cloud after separation and reconstruction. The experimental results showed that there was a high correlation between the actual value from manual measurement and the predicted value from the algorithm output. The accuracies of the main stem diameter, main stem length, and leaf length were 93.5%, 95.7%, and 83.8%, respectively, which meet the requirements of real applications. [ABSTRACT FROM AUTHOR]

Concerns have recently been expressed about the continuing availability of human bones from India, obtained originally for educational purposes but lacking the requisite informed consent that would be expected today. More generally, a broader claim is being made, namely, that the practice of using any unconsented bones in educational settings is unethical and should cease. These calls, in turn, raise broader issues regarding the availability of anonymous archival collections in anatomy museums. Although this debate centers on undergraduate anatomy teaching, much anthropological research utilizes human remains of past populations for which there can have been no consent. A suggested alternative for undergraduate teaching is the use of 3D images of human bones, rather than the bones themselves. In addressing these issues, the background to the India bone trade is assessed, and the year 1985 is pinpointed as having significant ethical weight. The cultural and ethical interests inherent in studying archival anonymous skeletal material are weighed against indiscriminate reburial. Although any use of unconsented material represents ethical compromise, account should be taken of changing ethical expectations with time. It is concluded that: there is no justification for repatriation or disposal of all bones for which specific informed consent has not been obtained; continued use of anonymous archival human bones in a professional setting is acceptable, even in the absence of informed consent, with the proviso that there are no culturally relevant groups seeking repatriation; the continued existence of bones in long‐standing private collections cannot be justified since it amounts to long‐term storage with no identified goals; the notion that 3D images are an ethically superior alternative to actual human bones is unsustainable, since there is an intimate connection between the bones and the 3D images. [ABSTRACT FROM AUTHOR]

Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field. [ABSTRACT FROM AUTHOR]

This paper presents a high-speed skeletonization algorithm for detecting the skeletons of linear objects from their binary images. The primary objective of our research is to achieve rapid extraction of the skeletons from binary images while maintaining accuracy for high-speed cameras. The proposed algorithm uses edge supervision and a branch detector to efficiently search inside the object, avoiding unnecessary computation on irrelevant pixels outside the object. Additionally, our algorithm addresses the challenge of self-intersections in linear objects with a branch detection module, which detects existing intersections and initializes new searches on emerging branches when necessary. Experiments on various binary images, such as numbers, ropes, and iron wires, demonstrated the reliability, accuracy, and efficiency of our approach. We compared the performance of our method with existing skeletonization techniques, showing its superiority in terms of speed, especially for larger image sizes. [ABSTRACT FROM AUTHOR]

The human interaction with an object is one of the most challenging domains in real-life applications, such as smart homes, surveillance, medical, education, safety-based application of computer vision, and artificial intelligence. In this research article, we have proposed a framework for human and object interaction in real-life examples such as sports and other activities. Initially, we reviewed video-based data by considering the three state-of-the-art data sets. Preprocessing steps have been followed to avoid extra costs, such as video-to-frame conversion, noise reduction and background subtraction. Human silhouette extraction has been performed via the Gaussian mixture model (GMM) and supper pixel model. Next, human body points and object location detection were performed. Finally, human and object-based features have been extracted. To minimize the features replication and to achieve optimized results, we have applied stochastic gradient descent and Restricted Boltzmann Machine; As a result, we have achieved an accuracy of 88.46%, 82.00%, and 88.30% on human body parts recognition over the MPII dataset, UCF_aerial dataset, and wild Dataset respectively. The classification accuracy for the MPII dataset is 92.71%, for the UCF_aerial dataset is 90.60%, and for sports video in the wild Dataset is 92.42%. We have achieved a high accuracy rate compared to other state-of-the-art methods and frameworks due to the complex feature extraction and optimization approach.

Transient analysis of multi-branched pipeline systems is generalized by the development of an impedance method. Both open and closed boundaries of branched pipeline elements were implemented in the analytical development of a reservoir multi-branched pipeline valve system, in which impedance and transient responses performance were compared with those of conventional approaches. To address realistic boundary conditions along the branched element, a partially opened boundary condition was implemented in the impedance expression of the branched pipeline system. The performance of the generalized multi-branch impedance method was evaluated on a large water supply system with 10 minor branches from the actual system. The impact of the designated branches was evaluated using the pressure root-mean-square error (RMSE) and the energy spectral density difference between the original and skeletonized systems. Combinations of multiple branches for certain flow conditions were identified based on the holistic response for both the frequency and time domains. The proposed method can be a useful alternative to effectively address the skeletonization issue for pipeline systems with multi-branched elements. [ABSTRACT FROM AUTHOR]

In this comprehensive study, a novel MATLAB to Python (M-to-PY) conversion process is showcased, specifically tailored for an intricate image skeletonization project involving fifteen MATLAB files and a large dataset. The central innovation of this research is the adept use of ChatGPT-4 as an AI assistant, pivotal in crafting a prototype M-to-PY converter. This converter’s capabilities were thoroughly evaluated using a set of test cases generated by the Bard bot, ensuring a robust and effective tool. The culmination of this effort was the development of the Skeleton App, adept at image sketching and skeletonization. This live and publicly available app underscores the enormous potential of AI in enhancing the transition of scientific research from MATLAB to Python. The study highlights the blend of AI’s computational prowess and human ingenuity in computational research, making significant strides in AI-assisted scientific exploration and tool development.

It is a well-known fact in the forensic community that the process of soft tissue decomposition lasts much longer when the cadaver is submerged in an aqueous environment. Studies of this nature with skeletal remains are less frequently produced, and therefore there is not much supported knowledge about whether human bones would face similar effects. The human skeleton potentially undergoes physical changes over various environmental conditions, but their changes in liquid conditions are poorly understood. This experiment was a study on the macroscopic effects that pH may have on the degradation of animal bones. Various small animal bones were placed in common household solutions of differing pH for the purpose of recording any physical changes. The only notable changes observed over the course of the experiment were the drastic discoloration of solutions with higher acidity, as well as the production of sediment-like residue in the saltwater solution. Further investigation into this in the context of potential body disposal could prove useful for the future of forensic science. [ABSTRACT FROM AUTHOR]

A digital twin describes the virtual representation of a real process. This twin is constantly updated with real data and can thus control and adapt the real model. Designing suitable digital twins for path planning of autonomous robots or drones is often challenging due to the large number of different dynamic environments and multi-task and agent systems. However, common path algorithms are often limited to two tasks and to finding shortest paths. In real applications, not only a short path but also the width of the passage with a path as centered as possible are crucial, since robotic systems are not ideal and require recalibration frequently. In this work, so-called homotopic shrinking is used to generate the digital twin, which can be used to extract all possible path proposals including their passage widths for 2D and 3D environments and multiple tasks and robots. The erosion of the environment is controlled by constraints such that the task stations, the robot or drone positions, and the topology of the environment are considered. Such a deterministic path algorithm can flexibly respond to changing environmental conditions and consider multiple tasks simultaneously for path generation. A distinctive feature of these paths is the central orientation to the non-passable areas, which can have significant benefits for worker and patient safety. The method is tested on 2D and 3D maps with different tasks, obstacles, and multiple robots. For example, the robust generation of the digital twin for a maze and also the dynamic adaptation in case of sudden changes in the environment is covered. This variety of use cases and the comparison with alternative methods result in significant advantages, such as high robustness, consideration of multiple targets, and high safety distances to obstacles and areas that cannot be traversed. Finally, it was shown that the environment for the digital twin can be reduced to reasonable paths by constrained shrinking, both for real 2D maps and for complex virtual 2D and 3D maps. [ABSTRACT FROM AUTHOR]

The paper analyses Peirce's hypoiconicity through the lens of Husserlian intentionality. Peirce's triple structure of hypoiconicity as resemblance relation, diagrammatical reasoning and metaphoric displacement is shown to require intentional acts in its production and interpretation. Regarding hypoiconicity as a semiotic schematization of Vorstellung, the paper places it in the context of Husserl's conception of intentionality in which iconicity appears as a stepping-stone towards the skeletonization of resemblance in diagrammatical abstraction and as schematic displacement in metaphor. As such, hypoiconic intentionality is argued to play a role also in Peirce's community conception of language. The paper's core claim is that intentionality provides an avenue for revealing hypoiconicity as a major, critical concept of semiotics, functioning as paradigm case for investigating the convergence of semiotics and phenomenology. [ABSTRACT FROM AUTHOR]

Although various structural optimization techniques have a sound mathematical basis, the structural robustness and practical constructability of optimal designs pose a great challenge in the manufacturing stage. This paper presents an automated novel approach stemming from structural optimization and engineering principles, where discrete members of the structurally optimized designs are driven towards optimal utilization. The developed workflow unifies topology, layout and size optimization in a single parametric platform, which subsequently outputs a ready-to-manufacture CAD skeletal model which can be manufactured either additively or by assembly. All such outputs are checked and validated for structural requirements; strength, stiffness and stability in accordance with standard codes of practice. In the implementations, first, a topology-optimal model is generated and converted to a one-pixel-wide chain model using skeletonization. Herein, this paper uses a novel efficient method to extract the skeleton by using pixel-padding near the domain borders. Secondly, a spatial frame is extracted from the skeleton for its member size and layout optimization. Finally, the CAD model is generated using constructive solid geometry trees and the structural integrity of each member is assessed to ensure structural robustness prior to manufacturing. Various examples presented in the paper showcase the validity of the presented workflow across various structural engineering applications. • A novel method for design-informed structural optimisation is developed. • Handles both additive and assembly-based manufacturing techniques. • Optimal design outputs are structurally safe, stable and adequately-stiff. • Automated ready-to-manufacture CAD model outputs improve design efficiency. [ABSTRACT FROM AUTHOR]

The tortuosity of retinal blood vessels is an important phenomenon, and it can act as a biomarker in the diagnosis of several eye diseases. The study of abnormalities in the tortuosity of retinal arteries and veins provides ophthalmologists with important information for disease diagnosis. Our study aims to compare the tortuosity relation between retinal arteries and veins by quantifying the vessels’ tortuosity in the retina using 14 tortuosity measures applied to the AV-classification retinal dataset. Two feature sets are created, one for arteries and the other for veins. The comparison between the tortuosity of arteries and veins is based on a two-sample T-test statistical method, a regression analysis between the quantified tortuosity features, principal component analysis at the dataset level, and the introduction of the arteriovenous length ratios concept to compare the variations in these new ratios to see the tortuosity behavior in each image. The methods’ results have shown that the tortuosity of retinal arteries and veins is similar. The result of the two-sample T-test supports the research hypothesis, as the P-value obtained was greater than 0.05. Furthermore, the regression analysis between arteries and veins features showed a high correlation (r2 = 89.39% and 89.11%) for arteries and veins, respectively. The study concludes that the retinal vessel type has no statistical significance in the tortuosity calculation results.

Abstract Internal thoracic arteries (ITAs) are the gold standard conduits for coronary revascularization because of their long-term patency and anti-atherosclerotic properties. Harvesting and preparation of ITAs for revascularization is a technically demanding procedure with multiple challenges. Over the last few decades, various methods and techniques for ITAs harvesting have been introduced by different surgeons and applied in clinical practice with different results. Harvesting of ITAs in pedicled or skeletonized fashion, with electrocautery or harmonic scalpel, with open or intact pleura, with clipping the end or keeping it perfused; papaverine delivery with intraluminal injection, perivascular injection, injecting into endothoracic fascia, and papaverine topical spray are the different techniques introduced by the number of researchers. At the same time, access to the ITAs for harvesting has also been studied. Access and harvesting through median sternotomy, mini anterolateral thoracotomy, thoracoscopic, and robotic-assisted harvesting of ITAs are the different techniques used in clinical practice. However, the single standard method for harvesting and preparation of ITAs has yet to be determined. In this review article, we aimed to discuss and analyze all these techniques of harvesting and preparing ITAs with the help of literature to find the best way for ITAs harvesting and preparation for myocardial revascularization.

Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multiresolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous’s computing framework, show how to use it, and characterize its performance and data storage. [ABSTRACT FROM AUTHOR]

This paper reviews a series of fast direct solution methods for electromagnetic scattering analysis, aiming to significantly alleviate the problems of slow or even non-convergence of iterative solvers and to provide a fast and robust numerical solution for integral equations. Then the advantages and applications of fast direct solution methods and the research trends are introduced in detail. Three different main methods are discussed, namely hierarchically off-diagonal low-rank matrices (HODLR) and skeletonization direct methods based on weak and strong admissibility condition. Numerical examples of computational complexity and electromagnetic scattering analysis of jet models are presented to demonstrate the efficiency and accuracy of each approach. Finally, a brief discussion is given on the main challenges and possible strategies of fast direct solution methods which still exist. [ABSTRACT FROM AUTHOR]

Thinning is a frequently used technique capable of producing all kinds of skeleton-like shape features in a topology-preserving way. It is an iterative object reduction: some border points of binary objects that satisfy some topological and geometrical constraints are deleted, and the entire process is repeated until stability is reached. In the conventional implementation of thinning algorithms, the deletability of all border points in the actual picture is to be investigated. That is why, we introduced the concept of k-attempt thinning ( k ≥ 1 ) in our previous work (presented in the 20th International Workshop on Combinatorial Image Analysis, IWCIA 2020). In the case of a k-attempt algorithm, if a border point 'survives' at least k successive iterations, it is 'immortal' (i.e., it cannot be deleted later). In this paper, we give a computationally efficient implementation scheme for 1-attempt thinning, and a 1-attempt 2D parallel thinning algorithm is reported. The advantage of the new implementation scheme over the conventional one is also illustrated. [ABSTRACT FROM AUTHOR]

Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist's judgment. Moreover, detecting the tortuosity severity of the retinal vessels', optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model. [ABSTRACT FROM AUTHOR]

Branch identification is key to the robotic pruning system for apple trees. High identification accuracy and the positioning of junction points between branch and trunk are important prerequisites for pruning with a robotic arm. Recently, with the development of deep learning, Transformer has been gradually applied to the field of computer vision and achieved good results. However, the effect of branch identification based on Transformer has not been verified so far. Taking Swin-T and Resnet50 as a backbone, this study detected and segmented the trunk, primary branch and support of apple trees on the basis of Mask R-CNN and Cascade Mask R-CNN. The results show that, when Intersection over Union (IoU) is 0.5, the bbox mAP and segm mAP of Cascade Mask R-CNN Swin-T are the highest, which are 0.943 and 0.940; as for the each category identification, Cascade Mask R-CNN Swin-T shows no significant difference with the other three algorithms in trunk and primary branch; when the identified object is a support, the bbox AP and segm AP of Cascade Mask R-CNN Swin-T is significantly higher than that of other algorithms, which are 0.879 and 0.893. Next, Cascade Mask R-CNN SW-T is combined with Zhang & Suen to obtain the junction point. Compared with the direct application of Zhang & Suen algorithm, the skeleton obtained by this method is advantaged by trunk diameter information, and its shape and junction points position are closer to the actual apple trees. This model and method can be applied to follow-up research and offer a new solution to the robotic pruning system for apple trees. [ABSTRACT FROM AUTHOR]

Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.

ABSTRACT Purpose To evaluate the retinal blood vascular network of the retinographies of patients with different grades of diabetic retinopathy. Methods Ninety Retinographies (MESSIDOR database) were used, with different grades of diabetic retinopathy divided into 4 groups: no retinopathy (n=23), grade one (n=20), grade two (n=20) and grade three (n=27) diabetic retinopathy. The grades of diabetic retinopathy were classified according to the number of microaneurysms, number of hemorrhages and the presence of neovascularization. The images were skeletonized and quantified by fractal methods: dimension of box-counting (Dbc) and information (Dinf). Results The means of Dbc values of groups were around 1.25, without statistically significant difference in the dimension values between groups for whole retina. There was also no statistical difference in Dinf values between groups, whose means ranged between 1.294 ± 0.013 (group of grade 1) and 1.3 ± 0.017 (group of grade 3). The retinographies were divided into regions of equal areas. The fractal values of some retinal regions showed statistical differences, but these differences were not enough to show the sensitivity of fractal methods in identifying diabetic retinopathy. Conclusion The fractal methods were not able to identify the different grades of diabetic retinopathy in retinographies.

Retinal vessel segmentation, skeletonization, and the generation of vessel segments are considered significant steps in any automated system for measuring the vessel biomarkers of several disease diagnoses. Most of the current tortuosity quantification methods rely on precise vascular segmentation and skeletonization of the retinal vessels. Additionally, the existence of a reference dataset for accurate vessel segment images is an essential need for implementing deep learning solutions and an automated system for measuring the vessel biomarkers of several disease diagnoses, especially for optimized quantification of vessel tortuosity or accurate measurement of AV-nicking. This study aimed to present an improved method for skeletonizing and extracting the retinal vessel segments from the 504 images in the AV classification dataset. The study utilized the Six Sigma process capability index, sigma level, and yield to measure the vessels’ tortuosity calculation improvement before and after optimizing the extracted vessels. As a result, the study showed that the sigma level for the vessel segment optimization improved from 2.7 to 4.39, the confirming yield improved from 88 percent to 99.77 percent, and the optimized vessel segments of the AV classification dataset retinal images are available in monochrome and colored formats.

This paper presents a high-speed skeletonization algorithm for detecting the skeletons of linear objects from their binary images. The primary objective of our research is to achieve rapid extraction of the skeletons from binary images while maintaining accuracy for high-speed cameras. The proposed algorithm uses edge supervision and a branch detector to efficiently search inside the object, avoiding unnecessary computation on irrelevant pixels outside the object. Additionally, our algorithm addresses the challenge of self-intersections in linear objects with a branch detection module, which detects existing intersections and initializes new searches on emerging branches when necessary. Experiments on various binary images, such as numbers, ropes, and iron wires, demonstrated the reliability, accuracy, and efficiency of our approach. We compared the performance of our method with existing skeletonization techniques, showing its superiority in terms of speed, especially for larger image sizes.

Over the past decade, deep learning frameworks such as convolutional neural networks (CNNs) have made major research inroads in upstream oil and gas, with applications in seismic processing/imaging, velocity model building, petrophysics, geological seismic interpretation, all the way to development, production and supply chain logistics. CNN fault prediction centers around the idea of image edge detection and, for improved prediction results, three data-driven steps are recommended. First, pre-condition the seismic data to increase signal-to-noise ratio as much as possible: iterative dip-steered median filtering and principal component filtering are adopted to further improve signal-to-noise ratio and sharpness for edge detection. Second, a fault probability volume obtained through deep learning (DL) -based fault detection using a U-Net architecture, taking synthetic seismic models as samples that can improve DL-based fault prediction in lieu of readily available labeled fault sets that may be prohibitively time-consuming to generate. Finally, edge enhancement is performed on the inference results to improve precision and fault continuity. A comparative analysis between related edge enhancement technologies is also presented. Results for three different faulting modes (normal, reverse and strike-slip) over three real seismic field data sets from China demonstrate the robustness of the proposed workflow. [ABSTRACT FROM AUTHOR]

A fast algorithm for solving large multi-scale scattering problems by the volume–surface integral equation (VSIE) is proposed. The sub-matrices of the near-field interactions (NFIs) in terms of the multi-level fast multipole algorithm (MLFMA) are compressed by the skeletonization after the theoretical and numerical investigations on their low-rankness are carried out. A block-diagonal preconditioner (BDP) based on the recursive skeletonization (RS) is developed to improve the conditioning of the impedance matrix of the VSIE. The rules to select hyperparameters in the proposed method are given. Numerical experiments conducted on scattering problems show the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Tree skeletons play an important role in tree structure analysis and 3D model reconstruction. However, it is a challenge to extract a skeleton from a tree point cloud with complex branches. In this paper, an automatic and fast tree skeleton extraction method (FTSEM) based on voxel thinning is proposed. In this method, a wood–leaf classification algorithm was introduced to filter leaf points for the reduction of the leaf interference on tree skeleton generation, tree voxel thinning was adopted to extract a raw tree skeleton quickly, and a breakpoint connection algorithm was used to improve the skeleton connectivity and completeness. Experiments were carried out in Haidian Park, Beijing, in which 24 trees were scanned and processed to obtain tree skeletons. The graph search algorithm (GSA) was used to extract tree skeletons based on the same datasets. Compared with the GSA method, the FTSEM method obtained more complete tree skeletons. The time cost of the FTSEM method was evaluated using the runtime and time per million points (TPMP). The runtime of FTSEM was from 1.0 s to 13.0 s, and the runtime of GSA was from 6.4 s to 309.3 s. The average value of TPMP was 1.8 s for FTSEM and 22.3 s for GSA, respectively. The experimental results demonstrate that the proposed method is feasible, robust, and fast with good potential for tree skeleton extraction. [ABSTRACT FROM AUTHOR]

Abstract Background In the context of biomarker discovery and molecular characterization of diseases, laser capture microdissection is a highly effective approach to extract disease-specific regions from complex, heterogeneous tissue samples. For the extraction to be successful, these regions have to satisfy certain constraints in size and shape and thus have to be decomposed into feasible fragments. Results We model this problem of constrained shape decomposition as the computation of optimal feasible decompositions of simple polygons. We use a skeleton-based approach and present an algorithmic framework that allows the implementation of various feasibility criteria as well as optimization goals. Motivated by our application, we consider different constraints and examine the resulting fragmentations. We evaluate our algorithm on lung tissue samples in comparison to a heuristic decomposition approach. Our method achieved a success rate of over 95% in the microdissection and tissue yield was increased by 10–30%. Conclusion We present a novel approach for constrained shape decomposition by demonstrating its advantages for the application in the microdissection of tissue samples. In comparison to the previous decomposition approach, the proposed method considerably increases the amount of successfully dissected tissue.

Morphological analysis of protein channels is a key step for a thorough understanding of their biological function and mechanism. In this respect, molecular dynamics (MD) is a very powerful tool, enabling the description of relevant biological events at the atomic level, which might elude experimental observations, and pointing to the molecular determinants thereof. In this work, we present a computational geometry-based approach for the characterization of the shape and dynamics of biological ion channels or pores to be used in combination with MD trajectories. This technique relies on the earliest works of Edelsbrunner and on the NanoShaper software, which makes use of the alpha shape theory to build the solvent-excluded surface of a molecular system in an aqueous solution. In this framework, a channel can be simply defined as a cavity with two entrances on the opposite sides of a molecule. Morphological characterization, which includes identification of the main axis, the corresponding local radius, and the detailed description of the global shape of the cavity, is integrated with a physico-chemical description of the surface facing the pore lumen. Remarkably, the possible existence or temporary appearance of fenestrations from the channel interior towards the outer lipid matrix is also accounted for. As a test case, we applied the present approach to the analysis of an engineered protein channel, the mechanosensitive channel of large conductance.

Background: Xylosyltransferases-I and II (XT-I and XT-II) catalyze the initial and rate limiting step of the proteoglycan (PG) biosynthesis and therefore have an import impact on the homeostasis of the extracellular matrix (ECM). The reason for the occurrence of two XT-isoforms in all higher organisms remains unknown and targeted genome-editing strategies could shed light on this issue. Methods: XT-I deficient neonatal normal human dermal fibroblasts were generated by using the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated proteins (Cas) 9 system. We analyzed if a reduced XT-I activity leads to abnormalities regarding ECM-composition, myofibroblast differentiation, cellular senescence and skeletal and cartilage tissue homeostasis. Results: We successfully introduced compound heterozygous deletions within exon 9 of the XYLT1 gene. Beside XYLT1, we detected altered gene-expression levels of further, inter alia ECM-related, genes. Our data further reveal a dramatically reduced XT-I protein activity. Abnormal myofibroblast-differentiation was demonstrated by elevated alpha-smooth muscle actin expression on both, mRNA- and protein level. In addition, wound-healing capability was slightly delayed. Furthermore, we observed an increased cellular-senescence of knockout cells and an altered expression of target genes knowing to be involved in skeletonization. Conclusion: Our data show the tremendous relevance of the XT-I isoform concerning myofibroblast-differentiation and ECM-homeostasis as well as the pathophysiology of skeletal disorders. [ABSTRACT FROM AUTHOR]

Hepatic vessel skeletonization serves as an important means of hepatic vascular analysis and vessel segmentation. This paper presents a survey of techniques and algorithms for hepatic vessel skeletonization in medical images. We summarized the latest developments and classical approaches in this field. These methods are classified into five categories according to their methodological characteristics. The overview and brief assessment of each category are provided in the corresponding chapters, respectively. We provide a comprehensive summary among the cited publications, image modalities and datasets from various aspects, which hope to reveal the pros and cons of every method, summarize its achievements and discuss the challenges and future trends. [ABSTRACT FROM AUTHOR]

Data visualization is a technique used to see the unseen information, that will help humanity to discover important things. There are many methods to represent the datasets, but data visualization is the best because it can preserve the information. Skeleton is used to analyse the visualization, thus data visualization gives efficient results. Comparison among well-known methods is the goal of this paper. The conclusion of this paper showed all the comparative results will be important for any further study. This study will use data visualization to discover novel dataset representations, which is the main goal of data visualization and can be useful in presenting necessary data. [ABSTRACT FROM AUTHOR]

Benefiting from the rapid development of electron microscopy imaging and deep learning technologies, an increasing number of brain image datasets with segmentation and synapse detection are published. Most of the automated segmentation methods label voxels rather than producing neuron skeletons directly. A further skeletonization step is necessary for quantitative morphological analysis. Currently, several tools are published for skeletonization as well as morphological and synaptic connectivity analysis using different computer languages and environments. Recently the Julia programming language, notable for elegant syntax and high performance, has gained rapid adoption in the scientific computing community. Here, we present a Julia package, called RealNeuralNetworks.jl, for efficient sparse skeletonization, morphological analysis, and synaptic connectivity analysis. Based on a large-scale Zebrafish segmentation dataset, we illustrate the software features by performing distributed skeletonization in Google Cloud, clustering the neurons using the NBLAST algorithm, combining morphological similarity and synaptic connectivity to study their relationship. We demonstrate that RealNeuralNetworks.jl is suitable for use in terabyte-scale electron microscopy image segmentation datasets. [ABSTRACT FROM AUTHOR]

Benefiting from the rapid development of electron microscopy imaging and deep learning technologies, an increasing number of brain image datasets with segmentation and synapse detection are published. Most of the automated segmentation methods label voxels rather than producing neuron skeletons directly. A further skeletonization step is necessary for quantitative morphological analysis. Currently, several tools are published for skeletonization as well as morphological and synaptic connectivity analysis using different computer languages and environments. Recently the Julia programming language, notable for elegant syntax and high performance, has gained rapid adoption in the scientific computing community. Here, we present a Julia package, called RealNeuralNetworks.jl, for efficient sparse skeletonization, morphological analysis, and synaptic connectivity analysis. Based on a large-scale Zebrafish segmentation dataset, we illustrate the software features by performing distributed skeletonization in Google Cloud, clustering the neurons using the NBLAST algorithm, combining morphological similarity and synaptic connectivity to study their relationship. We demonstrate that RealNeuralNetworks.jl is suitable for use in terabyte-scale electron microscopy image segmentation datasets.

ObjectiveTo compare performance assessment of two methods of measuring radial peripapillary capillary (RPC) vessel density (VD) after skeletonization using MATLAB and Image J in glaucoma clinical setting.MethodsSeventy-three eyes of 73 glaucoma patients from Beijing Tongren Hospital were included in this prospective study. Original images of RPC were obtained using optical coherence tomography angiography. Two approaches were executed before measuring. Method 1 (M1) required image sharpening, removal of big vessels, and skeletonization. Method 2 (M2) required skeletonization and removal of major vessels. Each method was executed twice. Repeatability and correlations with glaucomatous parameters were assessed. Factors associated with retinal nerve fiber layer thickness (RNFLT) and visual field mean deviation (MD) were analyzed.ResultsAverage VD was 13.86 ± 2.73 and 7.50 ± 2.50% measured by M1 and M2. Percentage of total elimination of the major vessels was 36.99 and 100% by M1 and M2, respectively. The intrasession and intersession reproducibility was higher by M2 (ICC = 0.979, ICC = 0.990) than by M1 (ICC = 0.930, ICC = 0.934). VD measured by M2 showed stronger correlations with glaucomatous parameters than by M1. By stepwise multiple linear regression, thinner RNFLT was associated with smaller VD measured by M2 (B = 4.643, P < 0.001). Worse MD was associated with smaller VD measured by M1 (B = 1.079, P = 0.015).ConclusionThe VD measured by M2 showed better reproducibility and higher correlation with glaucomatous structural parameters. Image sharpning helps display of hazy vasculature in glaucoma, which may reflect visual function better. Researchers should carefully choose image processing methods according to their research object.

A Skeleton that is extracted by a skeletonization algorithm from a binary image is useful for object description, matching, recognition and compression. The parallel thinning algorithm, one of the skeletonization algorithms is well known to have computational effciency. The main contribution of this paper is that we proposed a novel fully parallel thinning algorithm based on a comprehensive investigation of the well-known Zhang-Suen (ZS)-series algorithms and the one-pass thinning algorithm (OPTA)-series algorithms, which not only has good performance in terms of (8,4) connectivity preservation and single-pixel thickness, but also has the following qualities: it is more robust to the boundary noise than the OPTA-series algorithms and it is faster than the ZS-series algorithms in terms of thinning speed, as confirmed by the experiments presented in this paper. [ABSTRACT FROM AUTHOR]

Skeleton creation is an important phase in the character animation pipeline. However, handcrafting skeleton takes extensive labor time and domain knowledge. Automatic skeletonization provides a solution. However, most of the current approaches are far from real-time and lack the flexibility to control the skeleton complexity. In this paper, we present an efficient skeletonization method, which can be seamlessly integrated into the sketch-based modeling process in real-time. The method contains three steps: (i) local sub-skeleton extraction; (i i) sub-skeleton connection; and (i i i) global skeleton refinement. Firstly, the local skeleton is extracted from processed polygon stroke and forms a subpart along with the sub-mesh. Then, local sub-skeletons are connected according to the intersecting relationships and modeling sequence of subparts. Lastly, a global refinement method is proposed to gives users coarse-to-fine control on the connected skeleton. We demonstrate the effectiveness of our method on a variety of examples created from both novices and professionals. [Display omitted] • A real-time skeletonization algorithm for immediately constructing an animatable skeleton from user sketch. • A flexible solution for skeleton complexity control. • An easy-to-use system for creating animatable models featured by sketch-based shape modeling and automatic rigging. [ABSTRACT FROM AUTHOR]