Mathematical Morphology Operations Research Articles

Automatic Detection of Trees using Airborne LiDAR Data Based on Geometric Characteristics

Abstract. One of the essential factors in analyzing urban environments is the presence of trees. Thus, the development of automatic or semi-automatic tree detection strategies is important for monitoring and providing data for municipal authorities’ planning efforts. In this context, we propose an automatic method for detecting trees using LiDAR data collected by airborne platforms. The proposed strategy uses the omnivariance as a key attribute, which is estimated locally from eigenvalues. Additionally, it utilizes an adaptive process to determine the optimal radius, followed by successive filtering based on the majority filter and mathematical morphology operators. The effectiveness of the proposed approach was evaluated on six study areas from two distinct datasets (Presidente Prudente/Brazil and Palmerston/New Zealand). In general, the results indicate a completeness rate around 99% and a correctness rate around 91%, resulting in an average Fscore of 95%. These findings suggest that the proposed approach has potential to detect trees in urban regions using airborne LiDAR data. Compared to related works, the proposed strategy tends to have a better result in terms of completeness.

Comparing inpainting techniques for urban object restoration from orbital images

Abstract. Based on the comparison of three established inpainting techniques, namely Criminisi, Beltamio, and Galerne and Leclaire, our study aimed to identify the most effective method for road restoration after extraction and detection using a Mathematical Morphology operators combined with hybrid techniques of digital image processing in remote sensing images. While all techniques were evaluated based on both visual analysis and quantitative metrics, the Criminisi approach emerged as the better choice. Despite introducing some additional noise, this technique demonstrated superior performance in terms of Completeness and overall Quality, achieving approximately 95.23% and 94.56%, respectively. Its ability to accurately reconstruct linear geometries while effectively removing existing noise highlighted its suitability for road restoration tasks.

Optimized binarization algorithm-based method for the image recognition and characterization of explosion damage in rock masses

The quantitative analysis of rock mass damage is crucial in fields such as engineering geology, disaster prevention, mining, geotechnical engineering, and structural engineering. With the advancement and application of noncontact measurement technologies and fractal theory, image-based damage identification methods are gaining increasing importance. This paper presents an optimized binarization algorithm for identifying and characterizing damage zones in granite explosion images. The method involves filtering, mathematical morphology operations, and connectivity recognition to effectively remove background noise while preserving clear boundaries of the damaged areas. It accurately captures the explosion damage in granite, both in terms of damage morphology and characteristic parameters. Additionally, the coefficient of agreement (COA) is introduced to quantitatively assess the accuracy of different methods in identifying damaged areas. The experimental results show that, compared with commonly used methods such as Otsu's method, Bernsen's algorithm, Niblack's algorithm, Sauvola's algorithm, and the K-means image clustering algorithm, the proposed method performs better in terms of identification accuracy and parameter agreement, achieving COA values near 1 across diverse experimental environments. Furthermore, the proposed method excels in handling uneven lighting, mitigating interference from rock surface textures and explosion carbonization zones, and demonstrates significant robustness in complex scenarios. The findings of this paper provide insights into the integration of engineering geology and computer vision technology. They offer valuable references for damage identification in excavation damage zones (EDZs), geological disaster evaluation, and structural damage warning systems.

ViCE: An automated and quantitative program to assess intestinal tissue morphology

Background and objectiveThe tissue morphology of the intestinal surface is architecturally complex with finger-like projections called villi, and glandular structures called crypts. The ratio of villus height-to-crypt depth ratio (Vh:Cd) is used to quantitatively assess disease severity and response to therapy for intestinal enteropathies, such as celiac disease and is currently quantified manually. Given the time required, manual Vh:Cd measurements have largely been limited to clinical trials and are not used widely in clinical practice. We developed ViCE (Villus Crypt Evaluator), a user-friendly software that automatically quantifies histological parameters in standard hematoxylin and eosin-stained intestinal biopsies. MethodsViCE is based on mathematical morphology operations and is scale and staining agnostic. It evaluates tissue orientation, identifies geometrical structure, and outputs key tissue measurements. ResultsThe output measurements of Vh:Cd are concordant with manual quantifications across multiple datasets. ConclusionsThe underlying mathematical morphological approach for ViCE is robust, and reproducible and easily adaptable for measurement of morphological features in other tissues.

Applications of mathematical morphology operators in civil infrastructures

Civil infrastructures require a permanent attention and maintenance from the moment of commissioning to moment of demolition. One important aspect which is mandatory to be taken into consideration is crack detection. Cracks can appear during the lifetime of the civil infrastructure and require specialized personal for assessment. Depending of the civil infrastructure, this operation can require specialized skills (such as climbing). To overcome this issue with regards to specialized manpower, image processing is used. Nowadays, images can be easily acquired using an unmanned aircraft vehicle system known also as a drone. The main advantages of a drone for civil infrastructure image acquisition are: i) it can be operated at different heights, ii) rapid data collection, iii) cost and time savings, iv) user-friendly interface.The main purpose of our paper resides in improving the accuracy of the pre-trained neural networks when noisy images are used in civil works. Throughout our research, we used a dataset which contains three classes of images: with cracks, without cracks and with noise. To remove the noise presented in images mathematical morphology operators (MMO) are used. Our results reveal that using opening operator filter on a dataset of images which present civil infrastructure cracks outperform the results obtained using only pre-trained neural networks. In addition, the computation time is reduced.

A method for cleaning wind power anomaly data by combining image processing with community detection algorithms

Current methodologies for cleaning wind power anomaly data exhibit limited capabilities in identifying abnormal data within extensive datasets and struggle to accommodate the considerable variability and intricacy of wind farm data. Consequently, a method for cleaning wind power anomaly data by combining image processing with community detection algorithms (CWPAD-IPCDA) is proposed. To precisely identify and initially clean anomalous data, wind power curve (WPC) images are converted into graph structures, which employ the Louvain community recognition algorithm and graph- theoretic methods for community detection and segmentation. Furthermore, the mathematical morphology operation (MMO) determines the main part of the initially cleaned wind power curve images and maps them back to the normal wind power points to complete the final cleaning. The CWPAD-IPCDA method was applied to clean datasets from 25 wind turbines (WTs) in two wind farms in northwest China to validate its feasibility. A comparison was conducted using density-based spatial clustering of applications with noise (DBSCAN) algorithm, an improved isolation forest algorithm, and an image-based (IB) algorithm. The experimental results demonstrate that the CWPAD-IPCDA method surpasses the other three algorithms, achieving an approximately 7.23% higher average data cleaning rate. The mean value of the sum of the squared errors (SSE) of the dataset after cleaning is approximately 6.887 lower than that of the other algorithms. Moreover, the mean of overall accuracy, as measured by the F1-score, exceeds that of the other methods by approximately 10.49%; this indicates that the CWPAD-IPCDA method is more conducive to improving the accuracy and reliability of wind power curve modeling and wind farm power forecasting.

Principal Component Analysis–Based Feature Recognition Technology for Marine Plankton Images in IoT Systems

Abstract To improve the accuracy of traditional plankton image feature recognition techniques, a principal component analysis (PCA)–based method for plankton image feature recognition in Internet of Things (IoT) systems was designed and proposed. According to the characteristics of a microscopic image of marine living plankton with much noise, a noise suppression method based on wavelet is established to meet the needs of edge information retention and stability in image processing. At the same time, the adaptive threshold gray segmentation, mathematical morphology operation, and canny edge detection operator are combined to complete the marine plankton image segmentation. Based on the global feature and texture information of contours, the feature set is composed. At the same time, PCA is used to reduce the dimensionality of the feature space and complete the feature recognition of plankton images in the IoT system. The test results show that this method has higher accuracy, shorter recognition time, lower cost, and certain applicability.

Algorithm Based on Morphological Operators for Shortness Path Planning

The problem of finding the best path trajectory in a graph is highly complex due to its combinatorial nature, making it difficult to solve. Standard search algorithms focus on selecting the best path trajectory by introducing constraints to estimate a suitable solution, but this approach may overlook potentially better alternatives. Despite the number of restrictions and variables in path planning, no solution minimizes the computational resources used to reach the goal. To address this issue, a framework is proposed to compute the best trajectory in a graph by introducing the mathematical morphology concept. The framework builds a lattice over the graph space using mathematical morphology operators. The searching algorithm creates a metric space by applying the morphological covering operator to the graph and weighing the cost of traveling across the lattice. Ultimately, the cumulative traveling criterion creates the optimal path trajectory by selecting the minima/maxima cost. A test is introduced to validate the framework’s functionality, and a sample application is presented to validate its usefulness. The application uses the structure of the avenues as a graph. It proposes a computable approach to find the most suitable paths from a given start and destination reference. The results confirm that this is a generalized graph search framework based on morphological operators that can be compared to the Dijkstra approach.

An image segmentation of adhesive droplets based approach to assess the quality of pesticide spray

Pesticide spray is a widely used chemical control method to minimize biological disasters in the agriculture industry. It is important to evaluate the efficacy and the quality of a pesticide sprayer, however, it cannot be conveniently achieved due to the lack of accessibility to the sprayed droplets and the intensity of the associated work. This paper proposes a novel method, based on an image processing-based approach, to assess the spray quality. The proposed method uses a combination of algorithms and criteria functions; such as, the maximum between-cluster variance algorithm, area threshold criteria, roundness factor, mathematical morphology operations, and the optimized watershed algorithm, to segment and assess the dark blue adhesive droplet images on a water-sensitive paper, placed in the crop field. In this work, three kinds of evaluation experiments are considered: (i) the manual analysis via droplet counting and processing, (ii) the automatic analysis by the commercially available droplet analyzer by Shenzhen DJI Co. Ltd., and (iii) the novel image processing based method introduced in this paper. The experimental results show a better consistency between the introduced novel method and the manual method. The former, however, provides a convenient and rapid way to assess the spray quality. It comes with an assessment algorithm along with an embedded device that provides a hardware view of the spray quality.

Automated diagnosis of acute cerebral ischemic stroke lesions from diffusion-weighted magnetic resonance images in assessment with modified Rankin Scale

Purpose: Automated diagnosis of acute cerebral ischemic stroke lesions (ACISL) is an evolving science. Early detection and exact delineation of ACISL automatically from diffusion-weighted magnetic resonance (DWMR) images are crucial for initiating prompt treatment. Thus, this work aims to determine the appropriate slice out of 60 pieces using multi-fractal analysis (MFA) and to segment the lesions in DWMR images using a hybrid optimization method. Features extracted from the segmented images were clinically correlated with the modified Rankin Scale (mRS). Methods: Thirty-one real-time stroke patients’ images were collected from Rajiv Gandhi Government General Hospital, Chennai, India. Multiple MRI slices were taken from each patient and filtered using an anisotropic diffusion filter (ADF). These filtered images were skull-stripped automatically by the maximum entropy thresholding technique incorporating mathematical morphological operations (MEM). The multi-fractal analysis (MFA) identifies the prominent slice with the significant infarct lesion. An isodata algorithm that integrated differential evolution with the particle swarm optimization method based on Kapur’s (IDPK) and Otsu’s (IDPO) approaches was attempted to segment the ACISL. Finally, the geometric and moment features extracted from the segmented lesions categorized the stroke severity and were correlated with the mRS. Results: The findings of the experimental work confirm that the suggested IDPK approach achieved usual normalized values for image similarity indices such as Sokal-Michener Coefficient (98.51%), Roger-Tanimoto Coefficient (90.16%), Sokel-Sneath-2 (91.04%), and Sorenson Index (90.04%) are superior to IDPO. Statistical significance proved that the segmented lesions’ area (r = 0.820, p < 0.0001) and perimeter (r = 0.928, p < 0.0001) were strongly correlated with the mild and moderate criteria of mRS. Conclusion: The proposed work effectively detected ischemic stroke lesions and their severity within the studied image groups. It could be a promising and potential tool to aid radiologists in validating their diagnosis.

The Deep Hybrid Neural Network and an Application on Polyp Detection

Mathematical morphology and convolution operators are two different methods to extract the characteristics and structures of images. Over the past decades, Deep Convolutional Neural Networks (DCNN) have been proven to be more powerful than traditional image-processing approaches. In this paper, we propose a novel structure called Deep Hybrid Neural Network (DHNN) by taking advantage of the convolution and morphological neural layers. Its practical application to polyp detection in medical images is illustrated. For experimental completeness, we adopt nine polyp image datasets, including publicly available data and our own collected data. For performance comparisons, we select three backbone models. Experimental results show that our DHNN achieves the best performance in comparisons in terms of computational complexity and accurate performance.

Automatic Extraction Method of Landslide Based on Digital Elevation Model and Digital Orthophoto Map Data Combined in Complex Terrain

This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. Based on image-based supervised classification, the influence factor constraint analysis is used to further identify and delineate the landslide area. Three mathematical morphology operations—erosion, dilation, and opening—are then applied to automatically identify and extract landslides. Experimental results demonstrate that achieving an accuracy, precision, and recall of 98.02%, 85.24%, and 84.78% shows that it is possible to better avoid interference caused by complex terrain with rich features. High-resolution DEM and DOM data contain rich spectral and texture information. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management.

A novel deep learning model for diabetic retinopathy detection in retinal fundus images using pre-trained CNN and HWBLSTM

Diabetic retinopathy (DR) is a global visual indicator of diabetes that leads to blindness and loss of vision. Manual testing presents a more difficult task when attempting to detect DR due to the complexity and variances of DR. Early detection and treatment prevent the diabetic patients from visual loss. Also classifying the intensity and levels of DR is crucial to provide necessary treatment. This study develops a novel deep learning (DL) approach called He Weighted Bi-directional Long Short-term Memory (HWBLSTM) with an effective transfer learning technique for detecting DR from the RFI. The collected fundus images initially undergo preprocessing to improve their quality, which includes noise removal and contrast enhancement using a Hybrid Gaussian Filter and probability density Function-based Gamma Correction (HGFPDFGC) technique. The segmentation procedure divides the image into subgroups and is crucial for accurate detection and classification. The segmentation of the study initially removes the optical disk (OD) and blood vessels (BVs) from the preprocessed images using mathematical morphological operations. Next, it segments the retinal lesions from the OD and BV removed images using the Enhanced Grasshopper Optimization-based Region Growing Algorithm (EGORGA). Then, the features from the segmented retinal lesions are learned using a Squeeze Net (SQN), and the dimensionality reduction of the extracted features is done using the Modified Singular Value Decomposition (MSVD) approach. Finally, the classification is performed by employing the HWBLSTM approach, which classifies the DR abnormalities in datasets as non-DR (NDR), non-proliferative DR (NPDR), moderate NPDR (MDNPDR), and severe DR, also known as proliferative DR (PDR). The proposed approach is implemented on APTOS as well as MESSIDOR datasets. The outcomes proved that the proposed technique accurately identifies the DR with minimal computation overhead compared to the existing approaches. Communicated by Ramaswamy H. Sarma

Mathematical Morphological Operations for Quadri -Partitioned Neutrosophic Set

This research aims to introduce a novel notion in mathematical morphological operations on a quadri-partitioned neutrosophic set, which is a particular case of the neutrosophic refined set. In neutrosophic theory, the set is divided into three parts: the true set, the false set, and the indeterminancy set. The indeterminacy is studied in depth in quadri neutrosophic. The primary intention is to reduce uncertainty. The suggested study extracts the core concepts of morphological operations and explains their algebraic properties. Some features of morphological operators linked to quadri-partitioned neutrosophic sets are also derived.

Assessment of satellite-derived shorelines automatically extracted from Sentinel-2 imagery using SAET

The definition of the shoreline position from satellite imagery is of great interest among coastal monitoring techniques. Understanding the reality mapped by the resulting shorelines and defining their accuracy is of paramount importance. The assessment described in this paper constitutes a validation of the shorelines obtained by using the novel tool SAET (Shoreline Analysis and Extraction Tool) for automatic shoreline extraction. The resulting shorelines applying the different parameters available in SAET are assessed in 9 test sites with diverse morphology and oceanographic conditions along the Atlantic European and Western Mediterranean coasts. The reference data is obtained along large coastal segments (covering up to about 240 km) from nearly coincident very high-resolution satellite images.Different image processing levels and extraction methods have been tested, showing their key role in the accuracy of shoreline position. When defining the approximate shoreline position the Automated Water Extraction Index for images without shadows (AWEInsh) with a 0 threshold generally constitutes the best segmentation method. In turn, the employment of the mathematical morphological operations of dilation or erosion considerably improves the results in certain coastal typologies. On the contrary, the employment of atmospherically-corrected images has a smaller influence on the accuracy of the SDSs.Results support the idea that the magnitude of the errors is strongly related to the specific coastal conditions- In general, the lowest errors appear in low-energetic microtidal sites, contrary to the energetic and mesotidal coasts with gentle slopes.The shoreline errors range between 3.7 m and 13.5 m RMSE (root-mean-square error) among the different coastal types when selecting the most appropriate extraction parameters. The shoreline position identified with SAET shows a similar or better accuracy to that obtained by other tools.

Combining Adaptive Mathematical Morphology and Visual Reconstruction Theory for Animation Creation

Abstract This paper first discusses the use of traditional elements in animation creation and analyzes the linear narrative and artistic expression involved in its creation. Secondly, in order to carry out the image design of animation creation, the color adaptive mathematical morphology is used to carry out the image edge calculation, and the ICP algorithm in the visual reconstruction theory is introduced to carry out the point cloud alignment and fusion. Finally, the effectiveness of this paper’s method is evaluated by comparing algorithms and examples. According to the findings, the loss rate of expansion and corrosion details using the color adaptive mathematical morphology operator is 0.05% and 5.69%, respectively. The overall evaluation of the visual reconstruction presentation is 94.93% at grade C or above. This shows that using the method of this paper can accurately realize the image design of animation creation, enhance the audience’s perception of animation with a clearer view, and then promote the prosperity of the animation industry.

Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines

Overhead power transmission line icing (PTLI) disasters are one of the most severe dangers to power grid safety. Automatic iced transmission line identification is critical in various fields. However, existing methods primarily focus on the linear characteristics of transmission lines, employing a two-step process involving edge and line detection for PTLI identification. Nonetheless, these traditional methods are often complicated when confronted with challenges such as background noise or variations in illumination, leading to incomplete identification of the target area, missed target regions, or misclassification of background pixels as foreground. This paper proposes a new iced transmission line identification scheme to overcome this limitation. In the initial stage, we integrate the image restoration method with image filter enhancement to restore the image’s color information. This combined approach effectively retains valuable information and preserves the original image quality, thereby mitigating the noise presented during the image acquisition. Subsequently, in the second stage, we introduce an enhanced multi-threshold algorithm to separate background and target pixels. After image segmentation, we enhance the image and obtain the region of interest (ROI) through connected component labeling modification and mathematical morphology operations, eliminating background regions. Our proposed scheme achieves an accuracy value of 97.72%, a precision value of 96.24%, a recall value of 86.22%, and a specificity value of 99.48% based on the average value of test images. Through object segmentation and location, the proposed method can avoid background interference, effectively solve the problem of transmission line icing identification, and achieve 90% measurement accuracy compared to manual measurement on the collected PTLI dataset.

An efficient deep multi‐task learning structure for covid‐19 disease

AbstractCOVID‐19 has had a profound global impact, necessitating the development of infection detection systems based on machine learning. This paper presents a Multi‐task architecture that addresses the classification and segmentation tasks for COVID‐19 detection. The model comprises an encoder for feature representation, a decoder for segmentation, and a multi‐layer perceptron for classification. Evaluations conducted on two datasets demonstrate the model's performance in both classification and segmentation. To enhance efficiency and diagnosis accuracy, CT‐scan images undergo pre‐processing using image processing algorithms like histogram equalization, median filtering, and mathematical morphology operations. The combination of the median filter pre‐processing and the proposed model yields impressive results in the classification task, achieving high accuracy, sensitivity, and specificity, with values of 0.97, 0.97, and 0.96, respectively, for dataset 1, and 0.96 in mentioned metrics for dataset 2. For segmentation, the proposed model, particularly with the average morphology pre‐processing, exhibits excellent performance with high accuracy, low mean squared error, high peak signal‐to‐noise ratio, high structural similarity index, and a mean dice coefficient of 88.86 ± 0.05 for dataset 1, and 87.97 ± 0.02 for dataset 2. Furthermore, the pre‐trained models consistently demonstrate the superiority of the median filter and proposed model in the classification task on the same datasets. In conclusion, the proposed multi‐task model, incorporating image processing techniques, achieves remarkable results in both classification and segmentation. The utilization of pre‐processing algorithms and the multi‐task framework significantly contribute to superior performance metrics. This study encourages further exploration of combining diverse image processing algorithms to advance infection diagnosis and treatment.

Overlap Functions-Based Fuzzy Mathematical Morphological Operators and Their Applications in Image Edge Extraction

As special aggregation functions, overlap functions have been widely used in the soft computing field. In this work, with the aid of overlap functions, two new groups of fuzzy mathematical morphology (FMM) operators were proposed and applied to image processing, and they obtained better results than existing algorithms. First, based on overlap functions and structuring elements, the first group of new FMM operators (called OSFMM operators) was proposed, and their properties were systematically analyzed. With the implementation of OSFMM operators and the fuzzy C-means (FCM) algorithm, a new image edge extraction algorithm (called the OS-FCM algorithm) was proposed. Then, the second group of new FMM operators (called ORFMM operators) was proposed based on overlap functions and fuzzy relations. Another new image edge extraction algorithm (called OR-FCM algorithm) was proposed by using ORFMM operators and FCM algorithm. Finally, through the edge segmentation experiments of multiple standard images, the actual segmentation effects of the above-mentioned two algorithms and relevant algorithms were compared. The acquired results demonstrate that the image edge extraction algorithms proposed in this work can extract the complete edge of foreground objects on the basis of introducing the least noise.

An active contour model for image segmentation using morphology and nonlinear Poisson’s equation

Active contour model (ACM) is considered as a feasible tool to handle image segmentation problems via an unsupervised learning approach. This paper compensates the shortcomings of well-known ACMs to introduce erosion and dilation operations in mathematical morphology to fit the intensity differential functions of contours on both sides of the image. A nonlinear Poisson’s equation is developed to deal with the interference of nonuniform noise within the image, and finally build a new model. This paper develops a new image processing approach using morphology and nonlinear Poisson’s equation, which yields a fast and efficient algorithm among known similar image segmentation methods, and has a high level of accuracy in processing image segmentation.