Geometric Shape Analysis Research Articles

Segmentation of Individual Tree Points by Combining Marker-Controlled Watershed Segmentation and Spectral Clustering Optimization

Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with this problem, this paper introduces a novel approach that combines marker-controlled watershed segmentation with a spectral clustering algorithm. Initially, we determined the local maxima within a series of variable windows according to the lower bound of the prediction interval of the regression equation between tree crown radius and tree height to preliminarily segment individual trees. Subsequently, using this geometric shape analysis method, the under-segmented trees were identified. For these trees, vertical tree crown profile analysis was performed in multiple directions to detect potential treetops which were then considered as inputs for spectral clustering optimization. Our experiments across six plots showed that our method markedly surpasses traditional approaches, achieving an average Recall of 0.854, a Precision of 0.937, and an F1-score of 0.892.

Fully Automated Segmentation of Human Eyeball Using Three-Dimensional U-Net in T2 Magnetic Resonance Imaging.

To develop and validate a fully automated deep-learning-based tool for segmentation of the human eyeball using a three-dimensional (3D) U-Net, compare its performance to semiautomatic segmentation ground truth and a two-dimensional (2D) U-Net, and analyze age and sex differences in eyeball volume, as well as gaze-dependent volume consistency in normal subjects. We retrospectively collected 474 magnetic resonance imaging (MRI) scans, including different gazing scans, from 119 patients. A 10-fold cross-validation was applied to separate the dataset into training, test, and validation sets for both the 3D U-Net and 2D U-Net. Performance accuracy was measured using four quantitative metrics compared to the ground truth, and Bland-Altman plot analysis was conducted. Age and sex differences in eyeball volume and variability in eyeball volume differences across gazing directions were analyzed. The 3D U-Net outperformed the 2D U-Net with mean accuracy scores >0.95, showing acceptable agreement in the Bland-Altman plot analysis despite a tendency for slight overestimation (mean difference = -0.172 cm³). Significant sex differences and age effects on eyeball volume were observed for both methods (P < 0.05). No significant volume differences were found between the segmentation methods or within each method for the different gazing directions. Significant differences in performance accuracy were identified among the five gazing directions, with the upward direction showing a notably lower performance. Our study demonstrated the effectiveness of 3D U-Net human eyeball volume segmentation using T2-weighted MRI. The robustness and reliability of 3D U-Net across diverse populations and gaze directions support enhanced ophthalmic diagnosis and treatment strategies. Our findings demonstrate the feasibility of using the proposed 3D U-Net model for the automatic segmentation of the human eyeball, with potential applications in various ophthalmic research fields that require the analysis of 3D geometric eye globe shapes or eye movement detection.

Mobility Assessment Using Multi-Positional MRI in Children with Cranio-Vertebral Junction Anomalies.

This study aimed to assess the relevance of using multi-positional MRI (mMRI) to identify cranio-vertebral junction (CVJ) instability in pediatric patients with CVJ anomalies while determining objective mMRI criteria to detect this condition. Data from children with CVJ anomalies who underwent a mMRI between 2017 and 2021 were retrospectively reviewed. Mobility assessment using mMRI involved: (1) morphometric analysis using hierarchical clustering on principal component analysis (HCPCA) to identify clusters of patients by considering their mobility similarities, assessed through delta (Δ) values of occipito-cervical parameters measured on mMRI; and (2) morphological analysis based on dynamic geometric CVJ models and analysis of displacement vectors between flexion and extension. Receiver operating characteristics (ROC) curves were generated for occipito-cervical parameters to establish instability cut-off values. (3) Additionally, an anatomical qualitative analysis of the CVJ was performed to identify morphological criteria of instability. Forty-seven patients with CVJ anomalies were included (26 females, 21 males; mean age: 10.2 years [3-18]). HCPCA identified 2 clusters: cluster №1 (stable patients, n = 39) and cluster №2 (unstable patients, n = 8). ΔpB-C2 (pB-C2 line delta) at ≥2.5 mm (AUC 0.98) and ΔBAI (Basion-axis Interval delta) ≥ 3 mm (AUC 0.97) predicted instability with 88% sensibility and 95% specificity and 88% sensitivity and 85% specificity, respectively. Geometric CVJ shape analysis differentiated patients along a continuum, from a low to a high CVJ motion that was characterized by a subluxation of C1 in the anterior direction. Qualitative analysis found correlations between instability and C2 anomalies, including fusions with C3 (body p = 0.032; posterior arch p = 0.045; inferior articular facets p = 0.012; lateral mass p = 0.029). We identified a cluster of pediatric patients with CVJ instability among a cohort of CVJ anomalies that were characterized by morphometric parameters with corresponding cut-off values that could serve as objective mMRI criteria. These findings warrant further validation through prospective case-control studies.

Osteolytic Bone Loss and Skeletal Deformities in a Mouse Model for Early-Onset Paget's Disease of Bone with PFN1 Mutation Are Treatable by Alendronate.

A novel osteolytic disorder due to PFN1 mutation was discovered recently as early-onset Paget's disease of bone (PDB). Bone loss and pain in adult PDB patients have been treated using bisphosphonates. However, therapeutic strategies for this specific disorder have not been established. Here, we evaluated the efficiency of alendronate (ALN) on a mutant mouse line, recapitulating this disorder. Five-week-old conditional osteoclast-specific Pfn1-deficient mice (Pfn1-cKOOCL) and control littermates (33 females and 22 males) were injected with ALN (0.1 mg/kg) or vehicle twice weekly until 8 weeks of age. After euthanizing, bone histomorphometric parameters and skeletal deformities were analyzed using 3D μCT images and histological sections. Three weeks of ALN administration significantly improved bone mass at the distal femur, L3 vertebra, and nose in Pfn1-cKOOCL mice. Histologically increased osteoclasts with expanded distribution in the distal femur were normalized in these mice. Geometric bone shape analysis revealed a partial recovery from the distal femur deformity. A therapeutic dose of ALN from 5 to 8 weeks of age significantly improved systemic bone loss in Pfn1-cKOOCL mice and femoral bone deformity. Our study suggests that preventive treatment of bony deformity in early-onset PDB is feasible.

Using Geometric Morphometric Analysis of Magnetic Resonance Imaging to Assess the Anatomy of the Eustachian Tube in Children with and without Otitis Media.

Otitis media (OM) is among the most common of childhood illnesses. It has long been hypothesized that children under age two are predisposed to OM due to differences in the anatomy of the Eustachian tube (ET), including the angle of the ET. OM in later childhood is less common but does occur, begging the question, are there shape differences in the ET that persist underlying later occurrences of OM? To answer this question, a novel method, which applied geometric and morphometric shape analysis to landmarks obtained from MRI data, was used. MRI scans were performed on 16 children (5 control, 3 cOME, and 8 rAOM) between 2011 and 2015. Sixteen landmarks representing the shape of the ET, cranial base, and palate were analyzed. The results of a Procrustes ANOVA indicate that the shape of the ET varies significantly (p < 0.01) between the OM and control groups. The shape differences between the OM group and the control are a medial and low attachment site of the tensor veli palatini (TVP) muscle, a posterior and high torus tubarius, and an anteriorly projected palate. These results support previous findings that a relatively horizontal ET is associated with a predisposition for OM. This study used a novel approach to examine anatomical differences in children with and without OM. First, the data set is unique in that it includes MRI scans of children with a confirmed OM diagnosis. Second, the use of MRI scans in craniofacial anatomy OM research is novel and allows for the collection of soft tissue landmarks and the visualization of soft tissue structures. Third, geometric morphometric shape analysis is a statistical method that captures shape differences, offering a more universal picture of nuanced changes within the entire set of landmarks, in contrast to more traditional linear and angular measurements used in prior OM studies examining craniofacial anatomy.

Intrinsic Shape Analysis in Archaeology: A Case Study on Ancient Sundials

The fact that the physical shapes of man-made objects are subject to overlapping influences—such as technological, economic, geographic, and stylistic progressions—holds great information potential. On the other hand, it is also a major analytical challenge to uncover these overlapping trends and to disentagle them in an unbiased way. This article explores a novel mathematical approach to extract archaeological insights from ensembles of similar artifact shapes. We show that by considering all shape information in a find collection , it is possible to identify shape patterns that would be difficult to discern by considering the artifacts individually or by classifying shapes into predefined archaeological types and analyzing the associated distinguishing characteristics. Recently, a series of high-resolution digital representations of artifacts have become available. Such datasets enable the application of extremely sensitive and flexible methods of shape analysis. We explore this potential on a set of 3D models of ancient Greek and Roman sundials, with the aim of providing alternatives to the traditional archaeological method of “trend extraction by ordination” (typology). In the proposed approach, each 3D shape is represented as a point in a shape space —a high-dimensional, curved, non-Euclidean space. Proper consideration of its mathematical properties reduces bias in data analysis and thus improves analytical power. By performing regression in shape space, we find that for Roman sundials, the bend of the shadow-receiving surface of the sundials changes with the latitude of the location. This suggests that, apart from the inscribed hour lines, also a sundial’s shape was adjusted to the place of installation. As an example of more advanced inference, we use the identified trend to infer the latitude at which a sundial, whose location of installation is unknown, was placed. We also derive a novel method for differentiated morphological trend assertion, building upon and extending the theory of geometric statistics and shape analysis. Specifically, we present a regression-based method for statistical normalization of shapes that serves as a means of disentangling parameter-dependent effects (trends) and unexplained variability . In addition, we show that this approach is robust to noise in the digital reconstructions of the artifact shapes.

Competence-based approach to the topic «Area» in geometric origami modeling

Introduction. The article is devoted to the assertion of the need to develop and include in the educational process of growing personalities competence-oriented tasks provided by the technology of geometric origami modeling. Relevance. In modern mathematical education, there is an active search for improving the quality of education using competence- based approach.Purpose setting. In the scientific literature, as it turned out, no studies have been found indicating effective means of asserting a competence-based approach to the study of the topic «Area», which led to the need to develop and include in the educational process competence-oriented tasks designed within the framework of technology associated with the recognition, modification and evaluation of the area ratios of geometric shapes.Methodology and methods of the study. In theoretical studies, competence-based, environmental and synergetic, cognitive-visual approaches are considered. The method of conducting experimental work using visual and graphic means aimed at revealing the category of area as an additive scalar quantity is recreated from the analysis of geometric shapes arising during the geometric modeling of origami, which the child explores and sets their boundaries.Results. The effectiveness of the considered approach to the topic «Area» is proved.Conclusions. The scope of implementation of the research results is presented.

Active Licking Shapes Cortical Taste Coding.

Neurons in the gustatory cortex (GC) represent taste through time-varying changes in their spiking activity. The predominant view is that the neural firing rate represents the sole unit of taste information. It is currently not known whether the phase of spikes relative to lick timing is used by GC neurons for taste encoding. To address this question, we recorded spiking activity from >500 single GC neurons in male and female mice permitted to freely lick to receive four liquid gustatory stimuli and water. We developed a set of data analysis tools to determine the ability of GC neurons to discriminate gustatory information and then to quantify the degree to which this information exists in the spike rate versus the spike timing or phase relative to licks. These tools include machine learning algorithms for classification of spike trains and methods from geometric shape and functional data analysis. Our results show that while GC neurons primarily encode taste information using a rate code, the timing of spikes is also an important factor in taste discrimination. A further finding is that taste discrimination using spike timing is improved when the timing of licks is considered in the analysis. That is, the interlick phase of spiking provides more information than the absolute spike timing itself. Overall, our analysis demonstrates that the ability of GC neurons to distinguish among tastes is best when spike rate and timing is interpreted relative to the timing of licks.SIGNIFICANCE STATEMENT Neurons represent information from the outside world via changes in their number of action potentials (spikes) over time. This study examines how neurons in the mouse gustatory cortex (GC) encode taste information when gustatory stimuli are experienced through the active process of licking. We use electrophysiological recordings and data analysis tools to evaluate the ability of GC neurons to distinguish tastants and then to quantify the degree to which this information exists in the spike rate versus the spike timing relative to licks. We show that the neuron's ability to distinguish between tastes is higher when spike rate and timing are interpreted relative to the timing of licks, indicating that the lick cycle is a key factor for taste processing.

Long Hands gesture recognition system: 2 step gesture recognition with machine learning and geometric shape analysis

A two stage real-time hand gesture recognition system is presented. It combines a machine learning trained detection step with a colour processing contour shape validation step. The detection step is done with either Adaboost Cascades or Support Vector Machines using HOG features. The system achieves a low false positive rate and a sufficient true positive rate necessary for robust real-time performance. It performs well compared to MobileNets a state of the art Neural Network for mobile real-time applications.

First person – Kelly Diamond

ABSTRACTFirst Person is a series of interviews with the first authors of a selection of papers published in Biology Open, helping early-career researchers promote themselves alongside their papers. Kelly Diamond is first author on ‘ Computational anatomy and geometric shape analysis enables analysis of complex craniofacial phenotypes in zebrafish’, published in BiO. Kelly is a postdoc in the lab of Dr Murat Maga at the Center for Developmental Biology and Regenerative Medicine, Seattle Children's Research Institute, Seattle, USA, investigating which combination of factors contribute to organismal morphology.

Computational anatomy and geometric shape analysis enables analysis of complex craniofacial phenotypes in zebrafish.

ABSTRACTDue to the complexity of fish skulls, previous attempts to classify craniofacial phenotypes have relied on qualitative features or sparce 2D landmarks. In this work we aim to identify previously unknown 3D craniofacial phenotypes with a semiautomated pipeline in adult zebrafish mutants. We first estimate a synthetic ‘normative’ zebrafish template using MicroCT scans from a sample pool of wild-type animals using the Advanced Normalization Tools (ANTs). We apply a computational anatomy (CA) approach to quantify the phenotype of zebrafish with disruptions in bmp1a, a gene implicated in later skeletal development and whose human ortholog when disrupted is associated with Osteogenesis Imperfecta. Compared to controls, the bmp1a fish have larger otoliths, larger normalized centroid sizes, and exhibit shape differences concentrated around the operculum, anterior frontal, and posterior parietal bones. Moreover, bmp1a fish differ in the degree of asymmetry. Our CA approach offers a potential pipeline for high-throughput screening of complex fish craniofacial shape to discover novel phenotypes for which traditional landmarks are too sparce to detect. The current pipeline successfully identifies areas of variation in zebrafish mutants, which are an important model system for testing genome to phenome relationships in the study of development, evolution, and human diseases. This article has an associated First Person interview with the first author of the paper.

Laser Curve Extraction of Wheelset Based on Deep Learning Skeleton Extraction Network.

In this paper, a new algorithm for extracting the laser fringe center is proposed. Based on a deep learning skeleton extraction network, the laser stripe center can be extracted quickly and accurately. Skeleton extraction is the process of reducing the shape image to its approximate central axis representation while maintaining the image’s topological and geometric shape. Skeleton extraction is an important step in topological and geometric shape analysis. According to the characteristics of the wheelset laser curve dataset, a new skeleton extraction network, a hierarchical skeleton network (LuoNet), is proposed. The proposed architecture has three levels of the encoder–decoder network, and YE Module interconnection is designed between each level of the encoder and decoder network. In the wheelset laser curve dataset, the F1_score can reach 0.714. Compared with the traditional laser curve center extraction algorithm, the proposed LuoNet algorithm has the advantages of short running time, high accuracy, and stable extraction results.

A geometric completion and shape analysis method for damaged bilaterally symmetrical artifacts

• A technological framework is proposed for the restoration and analysis of a damaged bilaterally symmetrical artifact. • A hybrid non-rigid deformation approach is proposed to warp the reflection of the intact region to the damaged artifact. • Both geometric morphometrics and anthropometric measurements are used to capture the characteristic features. Archeological artifacts are important forms of tangible cultural heritage, providing evidence of cultural signification, human values, and the development of manufacturing technologies. The reconstruction of damaged bilaterally symmetrical artifacts is frequently undertaken for research and public display. However, traditional restoration technology requires physical intervention, which can cause secondary damage, and raise questions of authenticity. In this study, we proposed a hybrid non-rigid deformation approach to repair damaged bilaterally symmetrical artifacts based on a study of a heavily corroded metal mask of the Liao dynasty from the Palace Museum, China. In addition, since the mask is always placed over the head of the deceased person, it is an interesting question to capture the characteristic features. We employed geometric morphometrics (GM) and anthropometric measurements (AM) to investigate the geometric variations between the mask and human faces. The experimental results demonstrated the effectiveness of the proposed approach in the application of virtual restoration and shape analysis. These methods have wider application to other forms of archeological and anthropological data.

Computational anatomy and geometric shape analysis enables analysis of complex craniofacial phenotypes in zebrafish mutants

Computational anatomy (CA) approaches start by estimating a canonical template from a sample of images. This template is then used as a basis for statistical analysis to quantify structural differences among groups of interest. Due to the complexity of fish skulls, previous attempts to classify craniofacial phenotypes have relied on qualitative features or 2D landmarks. In this work we aim to identify and quantify differences in 3D craniofacial phenotypes in adult zebrafish mutants. We first estimate a synthetic normative zebrafish template using microCT scans from a sample pool of wildtype animals using the Advanced Normalization Tools (ANTs). To validate the accuracy of the template and our CA pipeline, we compared the otolith volumes derived from the CA approach to manually segmented volumes of the same set of zebrafish. Our overall CA based segmentation volumes are statistically similar to our manual segmentations and both results show that mutants have larger otoliths than their wildtype controls. We apply CA to zebrafish with somatic mutations in bmp1a, whose human ortholog when disrupted is associated with Osteogenesis Imperfecta. Our Generalized Procrustes Analysis separated the bmp1a and wildtype fish along the first two principal components which collectively explained 31.8% of the variation in the dataset. Compared to controls, the phenotypic differences in bmp1a fish are concentrated around the operculum and the orbit. Our CA approach seems to offer a potential pipeline for high throughput screening of complex fish craniofacial phenotypes, especially those of zebrafish which are an important model system for testing genome to phenome relationships in the study of development, evolution, and human diseases.

Gaze Parameters in the Analysis of Ambiguous Geometric Shapes.

This study explores perceptual organisation and shape perception when viewing a tetragon and an additional element (a dot) that is located at varying positions and distances next to the tetragon. The aim of the study is to determine the factors that can alter the interpretation of object configuration and impact whether the presented tetragon is perceived as a diamond or a square. Methods used in this study are a forced-choice task as a subjective measurement and eye tracking as an objective measurement of perceptual processes. Overall, 31 stimuli were presented to the participants: a tetragon in two different sizes with an additional element (a dot) located inside or outside the object at three different positions at three distances. The results indicate significant changes in shape perception, depending on the location of the additional element. The results are complemented with eye movement analysis indicating that as the distance between the elements increases, there is a higher probability of either of the two shape interpretations and the gaze is less likely to be directed to the area between the stimuli. Furthermore, the subjective perception of shape is codetermined by the shape perception when the tetragon is presented without the additional element.

Modified Lorenz Curve and Its Computation

The Lorenz curve is generally used to find out the inequality of income distribution. Mathematically a standard form of the Lorenz curve can be modified with the aim of simplicity of its symmetric analysis and calculation of the Gini coefficient that usually accompanies it. One way to modify the shape of the Lorenz curve without losing its characteristics but is simple in the analysis of geometric shapes is through a transformation (rotation). To be efficient and effective in computing and analyzing a Lorenz curve it is necessary to consider using computer software. In this article, in addition to describing the development of the concept of using transformations (rotations) of the standard Lorenz curve in an easy-to-do form, the symmetric analysis is also described by computational techniques using Mathematica® software. From the results of the application of the development of the concept of the Lorenz curve which is carried out on a data gives a simpler picture of the computational process with relatively similar computational results.

Investigation of cross-sectional image analysis method to determine the blending ratio of polyester/cotton yarn.

It has been considered as a great challenge to identify the blending ratio of polyester/cotton yarn in the field of textile industry. A new digital cross-sectional image processing method based on geometrical shape analysis is proposed to improve the measurement accuracy of polyester/cotton blend ratio. A self-developed microscope image capturing system is established to digitalise the cross-sectional images of polyester/cotton blended yarn. One set of image preprocessing algorithm is developed to conduct greyscale inversion, median filtering denoising and binarisation. The specially designed edge detection algorithm is used to identify the continuous profile of fibres. Finally, the roundness value of the cross-sectional fibre is calculated based on the proposed roundness algorithm, it can be used to identify the polyester/cotton fibres and calculate the blending ratio of them. Our experimental results show that the new digital analysis method proposed in this paper is feasible for the measurement of polyester/cotton blended ratio; therefore, it has a good application prospect in the field of textile quality control, including the development of new equipment, methods and standards.

Geometric Shape and Local Placement Analysis According to IFC Data of Building Elements

Geometric Shape and Local Placement Analysis According to IFC Data of Building Elements

SHAPE DESCRIPTOR FOR OBJECT CLASSIFICATION

In this article we propose an efficient modification of a previously published shape descriptor, which is fast and simple to compute. Proposed descriptor was developed for object classification and should be used with classifiers: SVM, KNN, etc. Object classification is a common task of computer vision, which has many applications in different areas: computer intelligence, robotic vision, smart cameras, autonomous driving, etc. Because the properties of objects are largely determined by their geometric features, shape analysis and classification are essential to almost every applied scientific and technological area. Main steps of the proposed algorithm are as follows: to find the object bounds; to smooth the bound contour using extremes based approach (if needed); to find side contour feature for each of N object rotation; to gather common feature vector; to classify object contour, using pre-trained classifier (SVM, KNN). In this work, in addition to method modifications (saving object proportions, rotation invariance, applying KNN classifier), we provide a wide comparison of our algorithm with existing approaches. The described method provided state-of-the-art performance on 100 leaves and Mpeg7 datasets, and showed good results on our own Mushroom dataset separately or together with texture or color based features.

Structural and free energy landscape of novel mutations in ribosomal protein S1 (rpsA) associated with pyrazinamide resistance

Resistance to key first-line drugs is a major hurdle to achieve the global end tuberculosis (TB) targets. A prodrug, pyrazinamide (PZA) is the only drug, effective in latent TB, recommended in drug resistance and susceptible Mycobacterium tuberculosis (MTB) isolates. The prodrug conversion into active form, pyrazinoic acid (POA), required the activity of pncA gene encoded pyrazinamidase (PZase). Although pncA mutations have been commonly associated with PZA resistance but a small number of resistance cases have been associated with mutationss in RpsA protein. Here in this study a total of 69 PZA resistance isolates have been sequenced for pncA mutations. However, samples that were found PZA resistant but pncA wild type (pncAWT), have been sequenced for rpsA and panD genes mutation. We repeated a drug susceptibility testing according to the WHO guidelines on 18 pncAWT MTB isolates. The rpsA and panD genes were sequenced. Out of total 69 PZA resistant isolates, 51 harbored 36 mutations in pncA gene (GeneBank Accession No. MH46111) while, fifteen different mutations including seven novel, were detected in the fourth S1 domain of RpsA known as C-terminal (MtRpsACTD) end. We did not detect any mutations in panD gene. Among the rpsA mutations, we investigated the molecular mechanism of resistance behind mutations, D342N, D343N, A344P, and I351F, present in the MtRpsACTD through molecular dynamic simulations (MD). WT showed a good drug binding affinity as compared to mutants (MTs), D342N, D343N, A344P, and I351F. Binding pocket volume, stability, and fluctuations have been altered whereas the total energy, protein folding, and geometric shape analysis further explored a significant variation between WT and MTs. In conclusion, mutations in MtRpsACTD might be involved to alter the RpsA activity, resulting in drug resistance. Such molecular mechanism behind resistance may provide a better insight into the resistance mechanism to achieve the global TB control targets.