Skeletonization Method Research Articles

Image-based truss recognition for density-based topology optimization approach

Topology optimization is a tool that supports the creativity of structural-designers and is used in various industries, from automotive to aeronautics, to reduce design iterations towards obtaining the optimal structure layout. However, this tool requires both time and experience to interpret the results into manufacturable and reliable structure layout. To improve this aspect, an interpretation support tool is being developed by our research team based on industrial knowledge and axiomatic design principles. This design tool will be very useful for aircraft structure development, for instance, as it aims to help the structural designer in the conception of stiffened panels. The tool has been divided into three modules: feature recognition, feature analysis and design support. This paper presents the first of the three modules that identifies the trusses of the optimized topology in terms of truss recognition algorithms. The purpose of the truss recognition algorithm is to translate the densities of the element of the optimized topology (low-level abstraction) to a skeletal structure (high-level abstraction) that contains nodes and branches that describe the same topology as the optimized topology. It should also ensure that the structural skeleton retains connectivity with loads and boundary conditions. The information may then be used by the design support tool for analysis, comparison, decision-making, design and optimization purposes. To do so, a novel image-based method using a binary skeleton is proposed. For this work, we identified multiple limitations existing in similar solutions and we mitigated them. Therefore, a new skeletonization method is proposed, which is specifically designed for truss recognition in the optimized topology. The capabilities of the skeletonization method are demonstrated by comparing it with existing methods, and the truss recognition algorithm is used with a test case exhibiting the algorithm’s capabilities on an airplane wing box rib.

THE TECHNIQUE OF HOMOTOPIC SKELETONIZATION OF BIT-MAPPED DRAWINGS OF PARTS OF SEA TRANSPORT

Context. Skeletonization is used in image processing of technical drawings, including drawings of sea transport parts, since the object’s skeleton reflects its topological structure. Сomparative analysis of the best methods of parallel topological skeletonization of the area objects, using spatial masks, showed that they give iterative distortions to the topology of primitives and their compositions. Therefore, the task of developing a technique for homotopic skeletonization of bit-mapped drawings of sea transport parts is relevant. Objective. To develope technique of improvement of topological equivalence of the skeletons to the сontour of sea transport parts, by means of gradual correction of typical skeleton’s distortions. Method. Сorrection of skeleton’s iterative distortions by modified spatial masks of the basic method of skeletonization and the reconstruction of the resulting skeleton by masks to restore its homotopy to the original, on the basis of developed reconstruction rules. Execution of the proposed technique was carried out on example of the basic method R.Y. Wu & W.H. Tsai. Results. The proposed technique is implemented as a program application that allows to perform quality skeletonization of images of drawings of sea transport parts. Conclusions. The shown examples of results of skeletonization of drawings of parts confirm efficiency of the proposed technique. The technique can be adapted to the methods of topological skeletonization of area objects, based upon application of spatial masks.

Hollow Pt-Functionalized SnO2 Hemipill Network Formation Using a Bacterial Skeleton for the Noninvasive Diagnosis of Diabetes.

Hollow-structured nanomaterials are presented as an outstanding sensing platform because of their unique combination of high porosity in both the micro- and nanoscale, their biocompatibility, and flexible template applicability. Herein, we introduce a bacterial skeleton method allowing for cost-effective fabrication with nanoscale precision. As a proof-of-concept, we fabricated a hollow SnO2 hemipill network (HSHN) and a hollow Pt-functionalized SnO2 hemipill network (HPN). A superior detecting capability of HPN toward acetone, a diabetes biomarker, was demonstrated at low concentration (200 ppb) under high humidity (RH 80%). The detection limit reaches 3.6 ppb, a level satisfying the minimum requirement for diabetes breath diagnosis. High selectivity of the HPN sensor against C6H6, C7H8, CO, and NO vapors is demonstrated using principal component analysis (PCA), suggesting new applications of HPN for human-activity monitoring and a personal healthcare tool for diagnosing diabetes. The skeleton method can be further employed to mimic nanostructures of biomaterials with unique functionality for broad applications.

Skeletonization application: Chinese calligraphy character representation and reconstruction

Chinese calligraphy is often used to perform the beauty of characters in Chinese culture and is quite suitable in the study of shape representation. The skeleton of a digital line pattern can be treated as the shape descriptor. However, the skeleton-biased and reconstruction-incomplete phenomena often exist in a skeletonization method, which results in the difficulty of using the skeleton to perform the beauty of Chinese calligraphy characters. To overcome this difficulty, skeletal line information derived from the skeletal points and indexed boundary points is defined, and its transformation is implemented by a procedure of two-phase skeletal line placement (SLP). Based on the SLP, an effective algorithm including the SLP-stroke for strokes, SLP-fork for forks, and SLP-end for the end parts of strokes is developed for constructing the skeletal line-based shape descriptor. Four indices of measurement of skeleton deviation, number of distorted forks, number of spurious strokes, and measurement of reconstructability are used to evaluate the performance of the proposed approach. Experimental results show that the skeleton-biased phenomenon can be greatly reduced and the pattern reconstructability close to 100% is achieved, thus confirming that the proposed skeletonization approach is suitable for the Chinese calligraphy character representation and reconstruction.

3D characterisation of the Fe-rich intermetallic phases in recycled Al alloys by synchrotron X-ray microtomography and skeletonisation

Synchrotron X-ray microtomography and skeletonisation method were used to study the true 3D network structures and morphologies of the Fe-rich intermetallic phases in recycled Al-5.0%Cu-0.6%Mn alloys with 0.5% and 1.0% Fe. It was found that, the Fe-phases in the 1.0%Fe alloy have node lengths of 5–25μm; while those in the 0.5%Fe alloy are of 3–17μm. The Fe-phases in the 1.0%Fe alloy also developed sharper mean curvature with wider distribution than those in the 0.5%Fe alloy. Combining SEM studies of the deeply-etched samples, the true 3D structures of 4 different type Fe phases in both alloys are also revealed and demonstrated.

Microscope image based fully automated stomata detection and pore measurement method for grapevines

BackgroundStomatal behavior in grapevines has been identified as a good indicator of the water stress level and overall health of the plant. Microscope images are often used to analyze stomatal behavior in plants. However, most of the current approaches involve manual measurement of stomatal features. The main aim of this research is to develop a fully automated stomata detection and pore measurement method for grapevines, taking microscope images as the input. The proposed approach, which employs machine learning and image processing techniques, can outperform available manual and semi-automatic methods used to identify and estimate stomatal morphological features.ResultsFirst, a cascade object detection learning algorithm is developed to correctly identify multiple stomata in a large microscopic image. Once the regions of interest which contain stomata are identified and extracted, a combination of image processing techniques are applied to estimate the pore dimensions of the stomata. The stomata detection approach was compared with an existing fully automated template matching technique and a semi-automatic maximum stable extremal regions approach, with the proposed method clearly surpassing the performance of the existing techniques with a precision of 91.68% and an F1-score of 0.85. Next, the morphological features of the detected stomata were measured. Contrary to existing approaches, the proposed image segmentation and skeletonization method allows us to estimate the pore dimensions even in cases where the stomatal pore boundary is only partially visible in the microscope image. A test conducted using 1267 images of stomata showed that the segmentation and skeletonization approach was able to correctly identify the stoma opening 86.27% of the time. Further comparisons made with manually traced stoma openings indicated that the proposed method is able to estimate stomata morphological features with accuracies of 89.03% for area, 94.06% for major axis length, 93.31% for minor axis length and 99.43% for eccentricity.ConclusionsThe proposed fully automated solution for stomata detection and measurement is able to produce results far superior to existing automatic and semi-automatic methods. This method not only produces a low number of false positives in the stomata detection stage, it can also accurately estimate the pore dimensions of partially incomplete stomata images. In addition, it can process thousands of stomata in minutes, eliminating the need for researchers to manually measure stomata, thereby accelerating the process of analysing plant health.

Fuzzy Object Skeletonization: Theory, Algorithms, and Applications.

Skeletonization offers a compact representation of an object while preserving important topological and geometrical features. Literature on skeletonization of binary objects is quite mature. However, challenges involved with skeletonization of fuzzy objects are mostly unanswered. This paper presents a new theory and algorithm of skeletonization for fuzzy objects, evaluates its performance, and demonstrates its applications. A formulation of fuzzy grassfire propagation is introduced; its relationships with fuzzy distance functions, level sets, and geodesics are discussed; and several new theoretical results are presented in the continuous space. A notion of collision-impact of fire-fronts at skeletal points is introduced, and its role in filtering noisy skeletal points is demonstrated. A fuzzy object skeletonization algorithm is developed using new notions of surface- and curve-skeletal voxels, digital collision-impact, filtering of noisy skeletal voxels, and continuity of skeletal surfaces. A skeletal noise pruning algorithm is presented using branch-level significance. Accuracy and robustness of the new algorithm are examined on computer-generated phantoms and micro- and conventional CT imaging of trabecular bone specimens. An application of fuzzy object skeletonization to compute structure-width at a low image resolution is demonstrated, and its ability to predict bone strength is examined. Finally, the performance of the new fuzzy object skeletonization algorithm is compared with two binary object skeletonization methods.

Skeleton matching with applications in severe weather detection

Severe weather conditions cause an enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of these destructive conditions such as damaging winds, hail, thunderstorms, and tornadoes. They are detected manually by meteorologists. In this paper, we propose an automatic framework to detect these patterns with high accuracy by introducing novel skeletonization and shape matching approaches. In this framework, first we extract regions with high probability of occurring bow echo from radar images and apply our skeletonization method to extract the skeleton of those regions. Next, we prune these skeletons using our innovative pruning scheme with fuzzy logic. Then, using our proposed shape descriptor, Skeleton Context, we can extract bow echo features from these skeletons in order to use them in shape matching algorithm and classification step. The output of classification indicates whether these regions are bow echo with over 97% accuracy.

Stiffness variability analysis of maize fiber bundles via multiscale simulation

The objective of this study was to analyze the stiffness variation of maize fiber bundles. Tensile modulus of fiber bundle was calculated by using a multiscale simulation. Fiber bundle cross section was reconstructed by the skeletonization method. The influences of the cell wall thickness, the microfibril angle (MFA) in S2 layer and the fiber diameter on the tensile modulus of retted maize bundles were determined. Numerical results showed that the MFA in S2 layer and the cell wall thickness in vascular bundle sheath (VBS) are the main factors affecting the tensile stiffness of maize bundle. When fiber diameter is fixed, the large variation of tensile modulus is attributed to the small MFA in S2 layer and the big difference in cell wall thickness of VBS. For fiber bundles in the skin group, the decreased tendency of tensile modulus is attributed to the decreased density of VBS when fiber diameter increases. However, for fiber bundles in the core group, the combined effects of decreased density of VBS and increased area ratio of Sv (transverse sectional area occupied by vessels and phloem tissue in fiber bundle) contribute more than 60% to the decreased tensile modulus. Compared to the increased area ratio of Sv, the decreased density of VBS exerts a greater influence on the decrease in stiffness. These findings can enhance our understanding of the large variation of natural fiber stiffness.

Aggregate Gradation Influence on Grouting Results and Mix Design of Asphalt Mixture Skeleton for Semi-Flexible Pavement

Abstract In this study, to design a satisfactory asphalt mixture skeleton for semi-flexible pavement (SFP), the effects of aggregate gradation on grouting results of porous asphalt mixture were analyzed based on the test results of 22 aggregate gradations. Moreover, the mix design method of asphalt mixture skeleton was also discussed. Results indicated that the grouting effects of asphalt mixture skeleton were affected by its internal void interconnectivity, including the morphological characteristics, structure, and size of pores, instead of the initial void volume (VV). The grouting effects of asphalt mixture skeleton were influenced by the fine aggregate at the specific particle size levels. An increase in the mixing proportion of coarse aggregates could improve the grouting effects. A valid mix design approach was developed to prepare the asphalt mixture skeleton of SFP. Finally, engineering properties of the grouted mixture specimens were validated. It was concluded that the aggregation gradation design was a very important aspect in effecting the grouting results of an asphalt mixture skeleton. Furthermore, the aggregate gradation should be selected to facilitate the cement slurry to permeate in full thickness of the asphalt mixture skeleton layer. This could be used to direct the design of asphalt mixture skeleton toward lessening field failures on the SFP.

An Automatic Tree Skeleton Extracting Method Based on Point Cloud of Terrestrial Laser Scanner

Tree skeleton could describe the shape and topological structure of a tree, which are useful to forest researchers. Terrestrial laser scanner (TLS) can scan trees with high accuracy and speed to acquire the point cloud data, which could be used to extract tree skeletons. An adaptive extracting method of tree skeleton based on the point cloud data of TLS was proposed in this paper. The point cloud data were segmented by artificial filtration and k-means clustering, and the point cloud data of trunk and branches remained to extract skeleton. Then the skeleton nodes were calculated by using breadth first search (BFS) method, quantifying method, and clustering method. Based on their connectivity, the skeleton nodes were connected to generate the tree skeleton, which would be smoothed by using Laplace smoothing method. In this paper, the point cloud data of a toona tree and peach tree were used to test the proposed method and for comparing the proposed method with the shortest path method to illustrate the robustness and superiority of the method. The experimental results showed that the shape of tree skeleton extracted was consistent with the real tree, which showed the method proposed in the paper is effective and feasible.

Exact Skeletonization Method in Water Distribution Systems for Hydraulic and Quality Models

A mathematical model is a powerful tool for simulating different scenarios that occur in a water distribution network without making physical experimentation. According to the objectives, a model can be classified into three categories: layout, design and operation. Furthermore, the level of detail is strongly related to the objective that the model tries to achieve. However, bigger amount of information does not mean better accuracy. For example, a fully detailed mathematical model of the network would lead to know every single connection. Usually, this information is so difficult to compile as imprecise. Therefore, one of the most important stages in elaborating a model consists of the model simplification, also known as skeletonization.During the works made for model skeletonization some assumptions are made. Most of the times, these assumptions may produce significant errors. Among the different techniques for network skeletonization, series pipe removal is one of the most used. It consists of replacing several adjacent pipes with a single one which must present the same head losses than the pipes being substituted. When there are no intermediate consumptions the problem has been effectively solved. The problem arises when a demand appears in one of the pipes being removed. It has been demonstrated that methods which assume constant roughness coefficients (either Hazen-Williams or Darcy equations) produce errors in the head losses. These errors may be even higher if travel time is included as a restriction, for example in water quality models.This paper reviews the most common techniques for serial pipes association. The error will be evaluated in both hydraulic and quality models. Finally, a method for exact substitution of serial pipes with intermediate demands is proposed. This method imposes two restrictions (head losses and travel time) and gives exact results when the flow direction is known. The method is tested with an example that highlights the results.

A Robust Skeletonization Method for Topological Complex Shapes

In this paper, we describe a skeletonization method effective and robust when applied to complex shapes, even if affected by boundary perturbations. This approach has been applied to binary segmented images containing bi-dimensional bounded shapes, generally not simply connected. It has been considered an external force field derived by an anisotropic flow. Through the divergence, we have examined the field flow at different times, discovering that the field divergence satisfies an anisotropic diffusion equation as well. Curves of positive divergence may be thought as propagating fronts converging to a steady state formed by shocks points. It has been proved that the sets of points, inside the shape, where divergence assumes positive values, converge to the skeleton. The curves with negative values of divergence remain static, so they may be directly used for edge extraction. This methodology has also been tested respect to boundary perturbations and disconnections.

Extraction of Dynamic Trajectory on Multi-Stroke Static Handwriting Images Using Loop Analysis and Skeletal Graph Model

The recovery of handwriting’s dynamic stroke is an effective method to help improve the accuracy of any handwriting’s authentication or verification system. The recovered trajectory can be considered as a dynamic feature of any static handwritten images. Capitalising on this temporal information can significantly increase the accuracy of the verification phase. Extraction of dynamic features from static handwritings remains a challenge due to the lack of temporal information as compared to the online methods. Previously, there are two typical approaches to recover the handwriting’s stroke. The first approach is based on the script’s skeleton. The skeletonisation method has highly computational efficiency whereas it often produces noisy artifacts and mismatches on the resulted skeleton. The second approach deals with the handwriting’s contour, crossing areas and overlaps using parametric representations of lines and thickness of strokes. This method can avoid the artifacts, but it requires complicated mathematical models and may lead to computational explosion. Our paper is based on the script’s extracted skeleton and provides an approach to processing static handwriting’s objects, including edges, vertices and loops, as the important aspects of any handwritten image. Our paper is also to provide analysing and classifying loops types and human’s natural writing behavior to improve the global construction of stroke order. Then, a detailed tracing algorithm on global stroke reconstruction is presented. The experimental results reveal the superiority of our method as compared with the existing ones.

Gradient vector fields based on variational image decomposition for skeletonization of electronic speckle pattern interferometry fringe patterns with variable density and their applications.

The skeletonization methods based on gradient vector fields (GVFs) have been a powerful tool for electronic speckle pattern interferometry (ESPI) fringe patterns. However, the skeletonization of ESPI fringe patterns with variable density has been an open problem in this area. In this paper, we propose a novel method for calculating GVFs based on the variational image decomposition of ESPI fringe patterns with variable density. In the proposed method, the GVFs of low-density regions are described in Beppo-Levi space, the high-density regions in Hilbert space and the noise regions in curvelet space. The GVFs of a whole image are the sum of the decomposed GVFs of low-density regions and high-density regions. The skeletons of ESPI fringe patterns with variable density can be obtained based on the topological analysis of the GVFs of a whole image. We apply the proposed method to a computer-simulated and two experimentally obtained ESPI fringe patterns with variable density and compare them with the related skeleton methods based on GVFs. The experimental results have demonstrated that the proposed method outperforms the other methods, even when the quality of the ESPI fringe patterns is considerably low.

Road centerlines extraction from high resolution images based on an improved directional segmentation and road probability

It is very important to extract accurate road networks from high resolution remote sensing images for various applications, such as transportation database updating. However, existing approaches cannot get satisfactory results. We propose an improved road networks extraction from remote sensing images based on the shear transform, the directional segmentation, the road probability, shape features and a skeletonization algorithm. The proposed method includes the following steps. First, we combine shear transform with directional segmentation to get the initial road regions. Second, road map based on Mahalanobis distance and thresholding is fused with the initial road regions to improve accuracy. Third, road shape features filtering and hole filling are used to extract reliable road segments. Finally, the road centerlines are extracted by an automatic subvoxel precise skeletonization method based on fast marching. Road networks are then generated by post-processing. Experimental results show that the proposed method can extract smooth and correct road centerlines.

Modified Alizarin Red S-Alcian Blue Staining for Reptilian Skeleton

<p>Skeletal staining is an important method in anatomical study. The aim of the research was to develop staining and clearing method of Reptilian skeleton using Alizarin Red S-Alcian Blue. The specimen were eviscerated, fixed, stained, cleared, and keep in glycerine solution. This modified double-staining has successfully stain bone and cartilage of Reptilian.</p>

Self noise and contrast controlled thinning of gray images

Homotopic grayscale thinning leads to bushy skeleton when applied on noisy images. One way to reduce this phenomenon is the use of the parametric thinning approach. It consists in relaxing the initial constraint by lowering low-contrast crests, peaks and ends, according to a manually selected parameter and under the constraint of ascendant gray level processing. In this work, we propose to control the thinning parameter by considering the lowering decision as a hypothesis testing of a statistical framework. A unitary hypothesis test based on the minimum test statistic is used for the elimination of noise-related peaks and extremities, while a fusion of multiple tests is performed for the insignificant crest lowering decision. This statistical control is first detailed under the assumption of additive Gaussian noise and then, is generalized for noise distributions with known pivotal quantity. The proposed statistical control leads to a local adjustment and a standardization of the parametric thinning process that depends on both the test significance level which is linked to image contrast and to noise standard deviation. The proposed method is tested on synthetic and real images, and compared to two skeletonization methods with proven efficiency.

Three-dimensional protein model similarity analysis based on salient shape index.

BackgroundProteins play a special role in bioinformatics. The surface shape of a protein, which is an important characteristic of the protein, defines a geometric and biochemical domain where the protein interacts with other proteins. The similarity analysis among protein models has become an important topic of protein analysis, by which it can reveal the structure and the function of proteins.ResultsIn this paper, a new protein similarity analysis method based on three-dimensional protein models is proposed. It constructs a feature matrix descriptor for each protein model combined by calculating the shape index (SI) and the related salient geometric feature (SGF), and then analyzes the protein model similarity by using this feature matrix and the extended grey relation analysis.ConclusionsWe compare our method to the Multi-resolution Reeb Graph (MRG) skeleton method, the L1-medial skeleton method and the local-diameter descriptor method. Experimental results show that our protein similarity analysis method is accurate and reliable while keeping the high computational efficiency.

Esqueletização multiescala para análise de forma de agregados

<p>A forma dos agregados minerais afeta várias propriedades das misturas asfálticas, dentre elas a resistência à de-formação permanente e à fadiga. A forma está diretamente relacionada com as proporções geométricas dos agregados. Vários são os métodos laboratoriais utilizados para determinar a forma dos agregados, mas frequentemente eles são trabalhosos e, por vezes, subjetivos. Neste trabalho, é apresentado o método de esqueletização multiescala para determinação das caracte-rísticas de forma dos agregados. Este método é simples de ser executado, e os resultados são de fácil entendimento. Neste método, as partículas são reduzidas a uma estrutura mais simples, chamada de esqueleto, em diferentes níveis de detalhamento (limiares). Dois tipos de agregados foram utilizados, basalto e seixo, com graus de esfericidade de 0,77 e 0,87, respectiva-mente, segundo a tabela Rittenhouse. As imagens dos agregados foram obtidas através de um scanner. Os esqueletos das imagens dos agregados foram obtidos utilizando a transformada distância, e os modelos de classificação foram obtidos a partir dos esqueletos por meio dos descritores de forma: raios médios, variância, número de ramos, tamanho do maior ramo e esfericidade. O melhor nível de detalhamento do esqueleto foi obtido empregando o valor de limiar igual a 5. O modelo de classificação apresentou 97% de acurácia quando empregado utilizando o algoritmo IBk. Este elevado índice de acerto de-monstra a robustez do método de esqueletização para a determinação e a classificação dos agregados quanto à forma.</p>