Neighborhood Structure Research Articles

Computed tomographic analysis of the frontal sinus morphology and structures that affect the size of the frontal sinus outflow tract in Vietnamese people

The frontal recess is a relatively small and narrow area with many neighboring structures and is very diverse in each body. Surgical intervention in this area is still challenging for surgeons. The International Frontal Sinus Anatomy Classification (IFAC) helps classify frontal cells on computed tomography (CT) scans for surgical purposes and anatomical accuracy. This study aims to investigate the morphology of the frontal sinus, the anteroposterior (A-P) diameter of the frontal sinus ostium and the A-P diameter of the frontal recess, the frontal cells classified by IFAC and the relationships between frontal sinus characteristics and A-P diameter of frontal sinus ostium and frontal recess on CT scans. Descriptive cross-sectional study on all CT scans of nasal cavities and paranasal sinuses of patients ≥ 20 years old at the Examination Department of Minh Duc Hospital — Ben Tre and Can Tho University of Medicine and Pharmacy Hospital. In terms of morphology, we found that the most common type was large-sized frontal sinus (40.7%), followed by medium (35.4%) and small (23.9%), respectively. The three dimensions of the frontal sinus in men were larger than in women (p<0.05). The mean A-P diameter of frontal sinus ostium was 6.41±2.66 mm. The mean A-P diameter of frontal sinus recess was 3.23 ± 2.59 mm. The agger nasi cells accounted for the highest proportion (93.6%) and supraorbital ethmoid cells accounted for the lowest proportion (2.9%). There was a statistically significant relationship between ostium diameter and frontal sinus morphology with p<0.001. The A-P diameter of the frontal sinus ostium and frontal recess became narrower with the appearance of the frontal cells. From the study’s results about the frontal sinus morphology, the relationship between the frontal recess cells and the structures affecting the anteroposterior diameter of the frontal sinus drainage pathway can assist surgeons in planning effective and safe surgical interventions in the frontal sinus area.

Spatial analysis and risk mapping of Crimean-Congo hemorrhagic fever (CCHF) in Sub-saharan Africa

Crimean Congo hemorrhagic fever (CCHF) is a re-emerging tick-borne zoonosis that is caused by CCHF virus (CCHFV). The geographical distribution of the disease and factors that influence its occurrence are poorly known. We analysed historical records on its outbreaks in various countries across the sub-Saharan Africa (SSA) to identify hotspots and determine socioecological and demographicfactors associated with these outbreaks. We used data from historical outbreaks that were reported between 1981 and 2022 in various countries in SSA. To develop a common framework for merging the outbreak data and potential explanatory variables, we generated a common shapefile that combined Level 2 administrative units in all the countries. Several climatic, environmental, socioecological data were obtained from on-line GIS databases and extracted using the shapefile. The data were analysed using an approximate Bayesian hierarchical model using the R-INLA package. The outcome was a Boolean variable which indicated whether an administrative unit in the shapefile was affected in a given year or not. A neighborhood structure was also generated and used to account for spatial autocorrelation in the analysis. The final model that was obtained from the analysis was used to build a CCHF risk map. A total of 54 CCHF outbreaks were compiled across 414 districts in nine SSA countries. Factors that were positively associated with CCHF outbreaks included human population density, land area under grassland, bare soil cover and shrub cover. Conversely, high precipitation during wet months, elevated mean temperature and slope had negative effects. The risk map generated shows that CCHF occurrence risk is higher in arid and semi-arid land (ASAL) of West Africa, the Sahelian region, Central Africa, and the Eastern and Southern Africa region. The analysis identified ecological and demographic factors that are associated with CCHF outbreaks in SSA. This finding suggests the need to improve surveillance for the disease especially in the grasslands where the human population is increasing.

A novel voting measure for identifying influential nodes in complex networks based on local structure

Identifying influential nodes in real networks is significant in studying and analyzing the structural as well as functional aspects of networks. VoteRank is a simple and effective algorithm to identify high-spreading nodes. The accuracy and monotonicity of the VoteRank algorithm are poor as the network topology fails to be taken into account.Given the nodes’ attributes and neighborhood structure, this paper put forward an algorithm based on the Edge Weighted VoteRank (EWV) for identifying influential nodes in the network. The proposed algorithm draws inspiration from human voting behavior and expresses the attractiveness of nodes to their first-order neighborhood using the weights of connecting edges. Similarity between nodes is introduced into the voting process, further enhancing the accuracy of the method. Additionally, this EWV algorithm addresses the problem of influential node clustering by reducing the voting ability of nodes in the second-order neighborhood of the most influential nodes. The validity of the presented algorithm is verified through experiments conducted on 12 different real networks of various sizes and structures, directly comparing it with 7 competing algorithms.Empirical results indicate a superiority of the presented algorithm over the remaining seven competing algorithms with respect to node differentiation ability, effectiveness, and ranked list accuracy.

Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology

BackgroundDeep learning (DL) has set new standards in cancer diagnosis, significantly enhancing the accuracy of automated classification of whole slide images (WSIs) derived from biopsied tissue samples. To enable DL models to process these large images, WSIs are typically divided into thousands of smaller tiles, each containing 10–50 cells. Multiple Instance Learning (MIL) is a commonly used approach, where WSIs are treated as bags comprising numerous tiles (instances) and only bag-level labels are provided during training. The model learns from these broad labels to extract more detailed, instance-level insights. However, biopsied sections often exhibit high intra- and inter-phenotypic heterogeneity, presenting a significant challenge for classification. To address this, many graph-based methods have been proposed, where each WSI is represented as a graph with tiles as nodes and edges defined by specific spatial relationships.ResultsIn this study, we investigate how different graph configurations, varying in connectivity and neighborhood structure, affect the performance of MIL models. We developed a novel pipeline, K-MIL, to evaluate the impact of contextual information on cell classification performance. By incorporating neighboring tiles into the analysis, we examined whether contextual information improves or impairs the network’s ability to identify patterns and features critical for accurate classification. Our experiments were conducted on two datasets: COLON cancer and UCSB datasets.ConclusionsOur results indicate that while incorporating more spatial context information generally improves model accuracy at both the bag and tile levels, the improvement at the tile level is not linear. In some instances, increasing spatial context leads to misclassification, suggesting that more context is not always beneficial. This finding highlights the need for careful consideration when incorporating spatial context information in digital pathology classification tasks.

Uncertainty modeling for inductive knowledge graph embedding.

In the process of refining Knowledge Graphs (KGs), new entities emerge, and old entities evolve, which usually updates their attribute information and neighborhood structures. This results in a distribution shift problem for entity features in the embedding space during graph representation learning. Most of existing inductive knowledge graph embedding methods focus mainly on the representation learning of new entities, neglecting the negative impact caused by distribution shift of entity features. In this paper, we use the skill of mean and variance reconstruction to develop a novel inductive knowledge graph embedding model named EDSU for processing the shift of entity feature distribution. Specifically, by assuming that the embedding feature of entity follows multivariate Gaussian distribution, the reconstruction combines the distribution characteristics of components in an entity embedding vector with neighborhood structure information of a set of entity embedding vectors, in order to alleviate the deviation of data information between intra-entity and inter-entity. Furthermore, the connection between the entity features distributions before and after the shift is established, which guides the model training process and provides an interpretation on the rationality of such handling distribution shift in view of distributional data augmentation. Extensive experiments have been conducted and the results demonstrate that our EDSU model outperforms previous state-of-the-art baseline models on inductive link prediction tasks.

You are entitled to access the full text of this document A novel hybrid algorithm of cooperative variable neighborhood search and constraint programming for flexible job shop scheduling problem with sequence dependent setup time

This study focuses on the flexible job shop scheduling problem with sequence-dependent setup times (FJSP-SDST), and the goal is minimizing the makespan. To solve FJSP-SDST, first, we develop a constraint programming (CP) model to obtain optimal solutions. Due to the NP-hardness of FJSP-SDST, a CP assisted meta-heuristic algorithm (C-VNS-CP) is designed to make use of the advantages of both CP model and cooperative variable neighborhood search (C-VNS). The C-VNS-CP algorithm consists of two stages. The first stage involves C-VNS, for which eight neighborhood structures are defined. In the second stage, CP is used to further optimize the good solution obtained from C-VNS. In order to prove the efficiency of the C-VNS algorithm, CP model, and C-VNS-CP algorithm, experiments of 20 instances are conducted.

Reinforcement learning-driven dual neighborhood structure artificial bee colony algorithm for continuous optimization problem

Reinforcement learning-driven dual neighborhood structure artificial bee colony algorithm for continuous optimization problem

PHYSICS BASED ALGORITHMS FOR FUZZY TRANSPORTATION PROBLEM

Transportation problems are computationally challenging due to their nondeterministic polynomial-time hard (NP-hard) nature, especially when uncertainties in costs, supply, and demand are involved. The fuzzy transportation problem addresses these uncertainties by representing them as fuzzy numbers, requiring robust methods for ranking and solving optimization models. Traditional exact methods become impractical for such problems, leading to the adoption of metaheuristic approaches. This study introduces three novel physics-based algorithms: one single-point-based and two population-based methods. These algorithms leverage principles from gravitational attraction, electromagnetism, and water flow dynamics, incorporating innovative neighborhood structures tailored to the fuzzy nature of transportation problems. The proposed methods were tested on diverse problem sizes and compared against established optimization techniques, such as genetic algorithms and commercial software, using Python implementations. An experimental design methodology was employed to optimize parameter tuning, enhancing computational efficiency and reducing experimentation time. Results demonstrate that the physics-based algorithms achieve competitive performance in terms of solution quality, computational efficiency, and adaptability to uncertainty. These findings underscore the potential of physics-based methods to advance optimization in fuzzy transportation and other domains with inherent uncertainties, paving the way for further research in hybrid and multi-objective approaches.

MORPHOGENESIS OF BRANCHIOGENIC GLANDS OF THE NECK IN THE FETAL PERIOD OF ONTOGENESIS

Branchiogenic glands play an important role in the control of calcium and iodine levels and thus have a direct impact on muscle contraction and neurotransmission. Variations in the structure and ectopic location of parathyroid and thyroid glands increase the complexity of thyroid and parathyroid surgery. Aim. To study the variants of structure and anatomical features of the parathyroid and thyroid glands at different stages of fetal development. Materials and methods. To study the anatomical variability of the branchiogenic glands of the neck in the fetal period of ontogenesis, we chose a set of classical methods of morphological research: morphometry, thin dissection under the control of a microscope, and photographic documentation. The material for the study were 48 preparations of human fetal cadavers from the 4th to the 10th month of intrauterine development. Digital parameters of organs and their correlation with neighboring organs and structures were grouped into appropriate age groups and processed by non-parametric methods of variation statistics with calculation of their arithmetic mean, its error and determination of probability of differences by Student's t-test using MS Excel and Biostatistics programs. The study was conducted in accordance with the main provisions of the Law of Ukraine ¹ 1801-XII "Fundamentals of the Legislation of Ukraine on Health Care", GCP (1996-2016), the Declaration of Helsinki of the World Medical Association for the Ethical Principles of Scientific Medical Research Involving Human Subjects (1964-2013), the Convention of the Council of Europe on Human Rights and Biomedicine (04.04.1997), Order of the Ministry of Health of Ukraine No. 690 dated 23.09.2009, and therefore concluded that the proposed study should be recommended for publication. The article is a fragment of the research work of the Department of Surgery № 1 of Bukovinian State Medical University: ‘Development, substantiation and implementation of new approaches to the diagnosis and treatment of some acute surgical diseases, prediction of their course and prevention of complications’, state registration number - 0121U110501: 01.2021-12.2025. Results. At the beginning of the fetal period of human ontogenesis there is an intensive development of the parathyroid glands. Variability of morphology of upper and lower parathyroid glands was found in 15 fetuses of 4-5 months of age. During this period, the following forms were observed: elongated, oval, bean-shaped, crescent-shaped. In the study of 15 fetuses at the age of 6-7 months, the following variations in the shape of the upper and lower parathyroid glands were found: oval, elongated, round, crescent, lenticular, bean-shaped. Topographically, we found the following variations in the location of the upper parathyroid glands: 1) between the upper and middle thirds of the thyroid lobes; 2) at the level of the upper third of the thyroid gland; 3) at the level of the middle third of the thyroid gland; 4) in the parenchyma of the thyroid gland; 5) above the thyroid lobes - 40% (20 cases), 36% (18 cases), 10% (5 cases), 8% (4 cases), 6% (3 cases), respectively. Conclusions. 1. The variants of the structure and topography of the thyroid gland, the upper parathyroid gland and the lower parathyroid gland, which we have identified, can be anatomical conditions for the development of both congenital pathology and variants of normal structure, which can nevertheless cause iatrogenic disorders during surgery. 2. In fetuses of 4-10 months of age, numerous variants of the structure and topography of the upper parathyroid gland and lower parathyroid gland are observed, which differ significantly from their typical structure. 3. The aplasia of the upper pineal gland detected in two human fetuses of 7 months of age is due to the peculiarities of their organogenesis and the formation of syntopia in the embryonic and prenatal periods of prenatal morphogenesis. 4. The obtained data on individual anatomical variability of the pineal glands should be taken into account by endocrinologists and ultrasonographers when performing diagnostic and surgical procedures.

URBAN FORM AND SOCIAL PRACTICES IN THREE NEIGHBOR-HOODS OF COLLECTIVE HOUSING IN THE CITY OF OUM EL BOUAGHI, ALGERIA

The aim of this article is to study the influence of urban form in different collective housing programs across three neighborhoods in the city of Oum El Bouaghi on user practices and social interactions. To understand the complex relationships and socio-spatial structure of neighborhoods, it is important to use mixed methods. This research is based on a combi-nation of two approaches: qualitative and quantitative. The latter is based on the space syn-tax method, using dedicated software called DepthMap©, specifically applying the visibility graph analysis (VGA). The qualitative approach is based on in-situ observation (static snap-shot method). The observation data from three neighborhoods were collected and synthesized on a map to superimpose them on that of the VGA. The results obtained from correlating observed social activities with syntactic measures show that urban form affects the use of outdoor spaces and can be considered a determining factor in social practices and interactions.

On Single-Valued Neutrosophic Soft Filter Convergence

The concept of single-valued neutrosophic soft filters plays a crucial role in the study of topological spaces. Extensive research has led to several generalizations of these filters, highlighting their significance in neutrosophic theory. This paper introduces a novel approach to single-valued neutrosophic soft filters, along with the idea of single-valued neutrosophic soft quasi-coincident neighborhood spaces, which are characterized by a unique interaction between the filters and quasi-coincident neighborhood structures. Additionally, we explore advanced neutrosophic theories, focusing on the properties and convergence of single-valued neutrosophic soft filters in soft topological spaces. Finally, we demonstrate the existence of product fuzzy soft filters.

A case report of rare variant: well-developed paracondylar process.

The paracondylar process (PCP) is defined as the bony prominence extending from the outer side of the condyles on the outer surface of the occipital bone downwards towards the transverse process of the atlas (TPA). In this case report, the morphometry of the rarely seen PCP and its morphometric data with neighboring structures are evaluated. Unilateral (right-sided) PCP was observed in the cranium of a Caucasian female. The base of the PCP, located on the outer side of the occipital condyle (OC) and just behind the jugular foramen (JF), was broader than its apex and had a molar shape. The base width was measured as 13.6, and the top width was measured as 8.7mm. PCP length was measured at 15.2mm. The base distance of the PCP, which was located very closely with the OC and JF, to these structures was measured as 0.42 and 0.59mm, respectively. The distance to the spinous process (SP) and mastoid process (MP) of the temporal bone was relatively greater and was measured as 1.07 and 15.5mm, respectively. The rare PCP reported, in this case, was well-developed and molar type. It is likely large enough to articulate with the TPA regarding length. However, this could not be shown because the matched atlas could not be found. We believe that supporting the close adjacencies of PCP with OC and JF with metric data may benefit clinicians working in regional surgery.

A Marginal Maximum Likelihood Approach for Hierarchical Simultaneous Autoregressive Models with Missing Data

Efficient estimation methods for simultaneous autoregressive (SAR) models with missing data in the response variable have been well explored in the literature. A common practice is introducing measurement error into SAR models to separate the noise component from the spatial process. However, prior studies have not considered incorporating measurement error into SAR models with missing data. Maximum likelihood estimation for such models, especially with large datasets, poses significant computational challenges. This paper proposes an efficient likelihood-based estimation method, the marginal maximum likelihood (ML), for estimating SAR models on large datasets with measurement errors and a high percentage of missing data in the response variable. The spatial autoregressive model (SAM) and the spatial error model (SEM), two popular SAR model types, are considered. The missing data mechanism is assumed to follow a missing-at-random (MAR) pattern. We propose a fast method for marginal ML estimation with a computational complexity of O(n3/2), where n is the total number of observations. This complexity applies when the spatial weight matrix is constructed based on a local neighbourhood structure. The effectiveness of the proposed methods is demonstrated through simulations and real-world data applications.

A multi-objective artificial bee colony algorithm for single machine scheduling with family setup under TOU tariffs

Time-of-use (TOU) electricity tariffs have been widely implemented in the manufacturing industry in many countries. This paper investigates a single machine scheduling problem involving incompatible job families with sequence-dependent setup times to minimise total electricity cost and total tardiness simultaneously. To tackle this problem, we propose a multi-objective artificial bee colony (MABC) algorithm. Utilising the dominance properties of the problem, we develop tailored heuristics aimed at improving the quality of initial food sources, and design multi-directional neighbourhood structures to explore desirable neighbour solutions along each objective direction. We construct a novel fitness function that not only considers Pareto rank but also incorporates the hypervolume contribution indicator to identify the promising solution space. Moreover, local integer programming is embedded into the MABC algorithm to intensify the search towards Pareto solutions. The experimental results indicate that the MABC algorithm performs significantly better than NSGA-II, SPEA2, and MOEA/D algorithms.

A Case Report of Mesenteric Panniculitis and Fibrous Dysplasia with Primary Metabolic Syndrome.

Mesenteric panniculitis (MP) is a rare clinically diagnosed disease resulting from fibro-inflammation in the intestinal adipose tissue. Precedent intra-abdominal inflammation or trauma is often evident. Fibrous dysplasia (FD) is a benign uncommon childhood disease of the bones, with fibrous connective tissue replacing bone tissue. FD can cause pain, disfigurement, and entrapment of critical neighbouring structures. Adult-onset craniofacial involvement is rare. Metabolic syndrome is characterized by obesity and chronic subclinical inflammation, promoting abnormal cellular fibrosis and proliferation. The concomitant incidence of MP and FD in an older female adult with metabolic syndrome is unprecedented. An aberrant metabolic contributory pathophysiology for both MP and FD could be postulated. A 60-year-old Caucasian woman with primary long-standing well-treated metabolic syndrome with obesity presented for revision bariatric surgery. She had previous intra-abdominal surgeries and occasional intermittent abdominal pain. The surgery was abandoned due to extensively inflamed intestinal mesentery. Radiological studies and intra-abdominal biopsy confirmed MP. She subsequently developed pronounced unilateral facial swelling and radiological studies confirmed FD. Treatment has been suboptimal for both MP and FD. She developed venous thromboembolism on tamoxifen treatment for MP. The patient currently reports abdominal pain and swelling from MP, and facial disfigurement, pain, and tinnitus from FD. Blood markers for inflammation have remained consistently high, and there is statin intolerance. Most recently liver steatosis and diabetes have developed. A case of MP and FD with primary metabolic syndrome is unique. An increased awareness of such rare cases could lead to further research, and critically needed treatments to optimize outcomes. Mesenteric panniculitis (MP) is a rare disease as is craniofacial fibrous dysplasia (FD) in adults. The incidence of these two rare diseases in an older adult with primary metabolic syndrome is previously unknown, making this case the first of its kind.Metabolic syndrome is common condition of the industrialized population, and an aberrant common "systemic" metabolic pathophysiology could contribute significantly to the inflammatory and fibrotic cellular changes inherent to both these two localized diseases of MP and FD.Knowledge about rare diseases, particularly if clustered with a primary systemic disease, could lead to better treatment outcomes in the future and promote novel therapeutic targets.

A dynamic artificial bee colony for fuzzy distributed energy-efficient hybrid flow shop scheduling with batch processing machines

Distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with batch processing machines (BPMs) is rarely considered, let alone DEHFSP with BPMs and uncertainty. In this study, a fuzzy DEHFSP with BPMs at a middle stage and no precedence between some stages is presented, and a dynamic artificial bee colony (DABC) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and fuzzy total energy consumption. To produce high quality solutions, Metropolis criterion is used, dynamic employed bee phase based on neighborhood structure dynamic selection is implemented, and group-based onlooker bee phase with bidirectional communication is given. Migration operator is also adopted to replace scout bee phase. Extensive experiments are conducted, and the optimal combination of key parameters for DABC is decided by the Taguchi method. Comparative results and statistical analysis show that new strategies of DABC are effective, and DABC is highly competitive in solving the considered fuzzy DEHFSP.

An effective inliers selection strategy for high-precision image feature matching

ABSTRACT Seeking a reliable inliers selection method to image feature matching is a fundamental and important task in image processing and robotic vision. To improve the precision of feature matching on putative matches with heavy outliers, we propose a new strategy to select the inliers from the rough feature correspondence-set. Firstly, we construct the inliers selection problem as a concise mathematical model with linear time complexity based on the local K-nearest neighbour structure preserving (KNNSP). Then, to improve the robustness of this model, we design a threshold decrease strategy for this model to iteratively solve the optimal inliers set. Extensive experiments on various image pairs demonstrate that the proposed method does not need prior information and can be directly used for rigid or non-rigid image matching problems. In addition, compared with other typical image matching algorithms, our method ensures competitive performance in precision and recall.

A novel neighborhood structure for flexible job shop scheduling problem considering Quality-Efficiency coupling effect

Balancing product quality and production efficiency in flexible job shop scheduling facilitates optimal allocation of time and resources, ultimately enhancing total profit. To address this, the flexible job shop scheduling problem considering quality–efficiency coupling effect (FJSP_QE) is investigated, and a novel neighborhood structure tailored to this problem is introduced in this paper. First, a mathematical model for FJSP_QE is established to accurately represent the total profit of a manufacturing enterprise under a given schedule. Second, to facilitate FJSP_QE solving, a quality–efficiency coupling neighborhood structure (QEN) with four processing time perturbation rules is proposed, and an improved genetic algorithm based on QEN is developed. Finally, benchmark instances with optional processing times are generated. Through numerical experiments, the superiority of the proposed QEN is verified in terms of solving efficiency and solution quality for FJSP_QE.

Adaptive gravitational clustering algorithm integrated with noise detection

Clustering analysis is frequently used in data mining, image processing, artificial intelligence, and so on. Traditional approaches heavily rely on manually configured parameters, of which the initial selection exerts a profound influence on the clustering outcomes. In addition, they usually only consider the relationship between two individual samples when calculating distances, neglecting the overall structure of the dataset, which can negatively affect clustering performance. At the same time, many contemporary algorithms are tailored to specific datasets, posing challenges in achieving optimal clustering performance for intricate, noisy datasets. To address these limitations, we propose an Adaptive Gravitational Clustering Algorithm Integrated with Noise Detection called GCIND. Inspired by the law of gravitation, GCIND takes into account the natural neighborhood structure of the entire dataset, adaptively computing the gravitation between data points by leveraging shared neighbors and Euclidean distance relationships. Our algorithm initially identifies and eliminates outliers or edge points in the dataset. It subsequently uses gravitation to autonomously cluster the remaining core data. Finally, the removed data are reallocated to their respective clusters. GCIND has four notable advantages: (1) it uses gravitation to build the neighborhood graph, reflecting the overall dataset structure; (2) it demonstrates stronger robustness in handling noisy datasets; (3) it uses adaptive gravitational neighborhood graph clustering, removing manual parameter tuning; (4) it adapts to complex manifold-structured datasets, offering broad applicability. Experiments have shown that GCIND, without requiring any parameter settings, demonstrates slightly better performance than the algorithms compared in the study, especially when dealing with complex manifold datasets.

A Similar Feature Point Matching Method for Aerial Electric Power Tower Images Based on a One-Ring Neighborhood of Vertices

Due to the susceptibility of images to various factors such as scale changes, imaging conditions, and image noise, traditional feature point matching methods are difficult to achieve ideal matching accuracy, leading to many challenges in the automatic analysis and processing of power tower images. To improve the matching accuracy of similar feature points in aerial images of electric power tower, this study proposes a method for matching similar feature points in aerial images of electric power tower based on a vertex ring neighborhood, addressing the problem of large matching errors caused by factors such as scale and imaging conditions. This method first adopts the feature point extraction technique of electric power tower image based on decomposition and filtering, and constructs the Laplacian pyramid of the original image. Subsequently, filter banks are used to decompose the pyramid image in different directions, extract local extremum points as candidate feature point sets, and merge them to obtain the final feature point set. On this basis, taking into account the spatial relationships and geometric characteristics around the feature points, a texture mapping algorithm based on vertex ring neighborhood and patch classification is applied to construct a vertex ring neighborhood structure and extract geometrically representative electric power tower features. Finally, by using a texture feature point matching method based on the principle of similarity, the similarity between the real-time image and the reference image features is calculated for matching, and combined with the Babbitt coefficient to remove mismatched feature points, accurate matching of similar feature points in aerial electric power tower images is achieved. The experimental results show that this method has a mean square error of less than 0.1Pixel in matching similar texture feature points of aerial power tower images under various working conditions, significantly improving the matching accuracy and providing an effective tool for automatic analysis and processing of power tower images.