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Infrared thermography is gaining relevance in breast cancer assessment. For this purpose, breast segmentation in thermograms is an important task for performing automatic image analysis and detecting possible temperature changes that indicate the presence of malignancy. However, it is not a simple task since the breast limit borders, especially the top borders, often have low contrast, making it difficult to isolate the breast area. Several algorithms have been proposed for breast segmentation, but these highly depend on the contrast at the lower breast borders and on filtering algorithms to remove false edges. This work focuses on taking advantage of the distinctive inframammary shape to simplify the definition of the lower breast border, regardless of the contrast level, which indeed also provides a strong anatomical reference to support the definition of the poorly marked upper boundary of the breasts, which has been one of the major challenges in the literature. In order to demonstrate viability of the proposed technique for an automatic breast segmentation, we applied it to a database with 180 thermograms and compared their results with those reported by others in the literature. We found that our approach achieved a high performance, in terms of Intersection over Union of 0.934, even higher than that reported by artificial intelligence algorithms. The performance is invariant to breast sizes and thermal contrast of the images. [ABSTRACT FROM AUTHOR]

Modelling the early evolution of magnetic flux ropes (MFRs) in the solar atmosphere is crucial for understanding their destabilization and eruption mechanism. Identifying the relevant magnetic field lines in simulation data, however, is not straightforward. In previous work an extraction and tracking method was developed to facilitate this task. Here, we present the corresponding graphical user interface (GUI), called GUITAR (GUI forTracking and Analysing flux Ropes), with the aim to offer a variety of tools to the community for identifying and tracking MFRs. The starting point is a map of a selected proxy parameter for MFRs, e.g., a map of the twist-parameter Tw, current density, etc. We showcase how the GUITAR tools can be used to disentangle a multi-MFR system and facilitate in-depth analysis of their properties and evolution by applying them on a time-dependent data-driven magnetofrictional model (TMFM) simulation of solar active region AR12473. We show the MFR extraction using Tw maps, together with targeted use of mathematical morphology algorithms and discuss the evolution of the system. [ABSTRACT FROM AUTHOR]

As a technique capable of replacing laboratory experiments, a large number of digital rock simulations have been widely used for the characterization of reservoir petrophysical parameters. For conditions with less coring data, rapid reconstruction of three-dimensional (3D) digital rocks using two-dimensional (2D) pore structure images is an important prerequisite for the accurate calculation of petrophysical parameters. However, the conventional digital rock rapid reconstruction method with poor pore connectivity leads to the erroneous evaluation of key reservoir rock parameters (e.g., permeability and resistivity). In this study, we used the sequential indicator simulation method as the base data and combined the erosion operation and expansion operation in mathematical morphology to realize the rapid construction of 3D digital rock models with strong pore connectivity. The accuracy of the digital rock model reconstructed by the new method was verified by comparing with the permeability and electrical properties obtained by the CT-based method, sequential indicator simulation method, multi-point statistical method, process-based method, and deep leaning method. This study overcomes the shortcomings of the sequential indicator simulation digital rock reconstruction method in terms of small pore radius and poor pore connectivity, improves the permeability of constructing 3D digital rocks, and lays the foundation for accurate and rapid analysis of petrophysical properties. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present basic concepts of lattice-valued fuzzy mathematical morphology. We use a complete residuated lattice as the codomain of fuzzy sets, a pair of fuzzy powerset operators, called the fuzzy erosion operator and the fuzzy dilation operator, is defined and their properties and relationships are studied. The pair of two operators forms a Galois adjunction and so that the induced fuzzy opening operator and fuzzy closing are an interior operator and a closure operator respectively. It is shown that the dilation stable sets and the erosion stable sets are equivalent, which form a fuzzy Alexandrov topology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, we measured the electrical potential distribution of surface dielectric barrier discharges (SDBD) caused by alternating voltage in synthetic air and nitrogen using the Pockels effect. It was shown that different potential distributions of filament thickness and length are generated in synthetic air and nitrogen. A filament analysis based on mathematical morphology was performed to quantify the geometric characteristics of the filaments produced in each gas atmosphere. The results show that the discharge filament thickness and the entropy in the filament direction depend on the oxygen ratio and the magnitude of the applied voltage. [ABSTRACT FROM AUTHOR]

The enhancement of horizontal boundaries of potential field sources is of great significance for the study of geological structures. Most of the existing filters for horizontal edges enhancement are presented based on the derivatives of potential field data, but these filters often show some shortcomings, for example, the output edges are divergent, strong and weak anomalies cannot be balanced, or some extra false edges appear in the results. In this paper, the edges enhancement filter MMA based on mathematical morphology is first defined by the combination of the basic operators of mathematical morphology. In order to make the filter have the ability to balance different amplitude anomalies, MMAZ and MMAT are proposed, and both of them can be used to delineate the horizontal edges of the field sources by their maxima. The experimental results of synthetic data show that MMAZ and MMAT have good equalization and anti-noise performances. Compared with several traditional filters, the horizontal edges of geological bodies obtained by these two filters are clearer and more convergent, and there are no extra edges in the output results for the complex situations that positive and negative anomalies exist simultaneously. Finally, the two new equalization filters are applied to gravity anomaly data in a potash mine area in Vientiane basin of Laos and the aeromagnetic anomaly of a metal mine in northwest China, the obtained positions of the structures are more accurate and clearer, which further illustrates the effectiveness of the two filtering techniques. [ABSTRACT FROM AUTHOR]

The scarcity of defect samples and the imbalance of defect types lead to the fact that achieving defect detection in color-patterned fabrics remains a challenge in the textile industry. Defect detection methods based on traditional auto-encoder are difficult to solve the problem of defect detection in complex color-patterned fabrics. In order to solve the problem of weak feature representation of traditional auto-encoders, this paper proposes an unsupervised method based on triplet attention multi-scale U-shape denoising convolutional auto-encoder (TA_MSUDCAE). The method further enhances the feature representation capability of the auto-encoder by introducing a triplet attention mechanism based on utilizing the multi-scale information of the image. Firstly, the defect-free samples that are added with Gaussian noise are used as inputs to the model in the training stage. The model is trained to reconstruct and repair the defective regions. Secondly, the test image is input to the trained model to obtain a normal reconstructed image, and the residual image is obtained by calculating the difference between the input image and the reconstructed image. Finally, the defect detection and localization results are obtained by threshold segmentation and mathematical morphology processing of the residual image. A large number of experiments have been carried out on a variety of representative color-patterned fabrics, and the results prove the effectiveness of the proposed method in fabric defect detection. [ABSTRACT FROM AUTHOR]

Gait abnormalities in older adults are linked to increased risks of falls, institutionalization, and mortality, necessitating accurate and frequent gait assessments beyond traditional clinical settings. Current methods, such as pressure-sensitive walkways, often lack the continuous natural environment monitoring needed to understand an individual's gait fully during their daily activities. To address this gap, we present a Lidar-based method capable of unobtrusively and continuously tracking human leg movements in diverse home-like environments, aiming to match the accuracy of a clinical reference measurement system. We developed a calibration-free step extraction algorithm based on mathematical morphology to realize Lidar-based gait analysis. Clinical gait parameters of 45 healthy individuals were measured using Lidar and reference systems (a pressure-sensitive walkway and a video recording system). Each participant participated in three predefined ambulation experiments by walking over the walkway. We observed linear relationships with strong positive correlations ( R 2 > 0.9 ) between the values of the gait parameters (step and stride length, step and stride time, cadence, and velocity) measured with the Lidar sensors and the pressure-sensitive walkway reference system. Moreover, the lower and upper 95% confidence intervals of all gait parameters were tight. The proposed algorithm can accurately derive gait parameters from Lidar data captured in home-like environments, with a performance not significantly less accurate than clinical reference systems. [ABSTRACT FROM AUTHOR]

The implementation of automated methods for sunspot detection is essential to obtain better objectivity, efficiency, and accuracy in identifying sunspots and analysing their morphological properties. A desired application is the contouring of sunspots. In this work, we construct sunspot contours from Solar Dynamics Observatory (SDO)/ Helioseismic and Magnetic Imager intensity images by means of an automated method based on development and application of mathematical morphology. The method is validated qualitatively – the resulting contours accurately delimit sunspots. Here, it is applied to high-resolution data (SDO intensitygrams) and validated quantitatively by illustrating a good agreement between the measured sunspot areas and the ones provided by two standard reference catalogues. The method appears to be robust for sunspot identification, and our analysis suggests its application to more complex and irregular-shaped solar structures, such as polarity inversion lines inside delta-sunspots. [ABSTRACT FROM AUTHOR]

An ultrasonic phased array defect extraction method based on adaptive region growth is proposed, aiming at problems such as difficulty in defect identification and extraction caused by noise interference and complex structure of the detected object during ultrasonic phased array detection. First, bilateral filtering and grayscale processing techniques are employed for the purpose of noise reduction and initial data processing. Following this, the maximum sound pressure within the designated focusing region serves as the seed point. An adaptive region iteration method is subsequently employed to execute automatic threshold capture and region growth. In addition, mathematical morphology is applied to extract the processed defect features. In the final stage, two sets of B-scan images depicting hole defects of varying sizes are utilized for experimental validation of the proposed algorithm's effectiveness and applicability. The defect features extracted through this algorithm are then compared and analyzed alongside the histogram threshold method, Otsu method, K-means clustering algorithm, and a modified iterative method. The results reveal that the margin of error between the measured results and the actual defect sizes is less than 13%, representing a significant enhancement in the precision of defect feature extraction. Consequently, this method establishes a dependable foundation of data for subsequent tasks, such as defect localization and quantitative and qualitative analysis. [ABSTRACT FROM AUTHOR]

Aiming at the problem of feature extraction of ship-radiated noise in complex marine environment, an adaptive weighted multiscale mathematical morphological filtering method is proposed. The weight coefficients of the structure elements of different scales in multiscale morphological filtering are determined adaptively by an improved particle swarm optimization algorithm, and the Teager energy kurtosis is used as the evaluation index of the filtered optimal signal to provide optimized weights for each scale. Finally, the optimized multiscale mathematical morphology filter with selective adaptive weights is bound and applied to the feature extraction of ship-radiated noise. The analysis results of simulated signal and measured ship-radiated noise show that this method has strong noise suppression and feature extraction ability, and the calculation is simple and fast, which provides an effective method for ship feature extraction. [ABSTRACT FROM AUTHOR]

BACKGROUND: By providing both functional and anatomical information from a single scan, digital imaging technologies like PET/CT and PET/MRI hybrids are gaining popularity in medical imaging industry. In clinical practice, the median value (SUVmed) receives less attention owing to disagreements surrounding what defines a lesion, but the SUVmax value, which is a semi-quantitative statistic used to analyse PET and PET/CT images, is commonly used to evaluate lesions. OBJECTIVE: This study aims to build an image processing technique with the purpose of automatically detecting and isolating lesions in PET/CT images, as well as measuring and assessing the SUVmed. METHODS: The pictures are separated into their respective lesions using mathematical morphology and the crescent region, which are both part of the image processing method. In this research, a total of 18 different pictures of lesions were evaluated. RESULTS: The findings of the study reveal that the threshold is satisfied by both the SUVmax and the SUVmed for most of the lesion types. However, in six instances, the SUVmax and SUVmed values are found to be in different courts. CONCLUSION: The new information revealed by this study needs to be further investigated to determine if it has any practical value in diagnosing and monitoring lesions. However, results of this study suggest that SUVmed should receive more attention in the evaluation of lesions in PET and CT images. [ABSTRACT FROM AUTHOR]

First-arrival picking is a fundamental and challenging task in seismic data processing. Existing algorithms such as the cross-correlation algorithm (CCT) struggle to achieve satisfactory noise immunity while maintaining picking efficiency. In this paper, we propose the first-arrival picking through mathematical morphology and edge detection (FPME) algorithm with three steps. The converting step transforms the original data into a binary image though a filtering technique, where the binary image represents the global energy distribution of high-energy and low-energy samples. The rendering step delimits the signal zone in the binary image through morphological operations, where the signal zone eliminates the noise in the upper part. The picking step detects first arrivals in the signal zone through the edge detection methods, where an evaluation function is designed to select the optimal detection results as first arrivals. Experiments were performed on four field data sets. The results demonstrated that FPME is more stable and reliable than five classic and popular algorithms. [ABSTRACT FROM AUTHOR]

Mathematical morphology (MM) is a powerful tool for spatial multispectral and hyperspectral image analyses. However, MM was originally developed for single-band images in which each pixel is represented by a numerical value. The most commonly used method for extending MM to multiband images is to process each band independently without considering its correlations with other bands. This can lead to the creation of artificial false spectral signatures and result in object misidentification. Therefore, extending MM to multiband images requires the use of an adequate vector ordering strategy to fully exploit its potential. This work proposes new vector ordering algorithms for the computation of multivalued MM. A multicriteria analysis (MCA) system is used as a tool for establishing an ordering of vectors. Two MCA approaches, namely, an ”analytic hierarchy process” and a ”preference ranking organization method for enrichment evaluation,” are developed to define ordering relations between vectors. To ensure the validity of the proposed vector ordering algorithms, the computed multivalued morphological profiles are compared using the proposed vector ordering approaches and conventional schemes. The results of applying the proposed vector ordering algorithms for computing morphological profiles show that good classification accuracies were achieved for urban structures in ROSIS hyperspectral images. [ABSTRACT FROM AUTHOR]

This paper reports a new variant of the model of the morphological pattern for thermokarst plains with fluvial erosion involving numerous remote sensing data. This variant of the model takes into account different trends of the thermokarst processes for watershed plains and lowered bottoms of khasyreis (drained thermokarst lakes). The model is tested at eight key sites differing in physiography, climate, and geocryology. Testing is based on high-resolution remote sensing data. The model is verified with a statistical analysis of lake area distributions within both watershed plain surface and lowered bottoms of khasyreis, demonstrating a state of dynamic balance in the course of emergence, growth, and drainage of the thermokarst lakes. The analysis shows that the morphological pattern of the thermokarst plains with fluvial erosion changes evolutionarily, with a reduction in the area of the main watershed surface according to a dependence close to exponential; the specific type of the dependence results primarily from the ratio of the lake generation density and the location density of the fluvial sources. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and study the NeutroAlgebra structure and many of operations and properties of the mathematical morphology. This is a generalization of the operations of fuzzy and classical mathematical morphology. An explanation of the new given operations is provided through several examples and experimental results. Since mathematical morphology deals with forms and is used in image processing, we consider in this research the Indeterminate Image (i.e. image with missing, unclear, or overlapping pixels), whose basic morphological operator's dilation, erosion, opening and closing transform an indeterminate image into another indeterminate image. Therefore, in fact, we deal with neutro-dilation, neutro-erosion, neutro-opening and neutro-closing. For a determinate image (i.e. image with no indeterminacy), the classical morphological operators transform it also into a determinate image, while the neutro-morphological operators into an indeterminate image. All work from below is available for both the indeterminate and determinate image. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and study the NeutroAlgebra structure and many of operations and properties of the mathematical morphology. This is a generalization of the operations of fuzzy and classical mathematical morphology. An explanation of the new given operations is provided through several examples and experimental results. Since mathematical morphology deals with forms and is used in image processing, we consider in this research the Indeterminate Image (i.e. image with missing, unclear, or overlapping pixels), whose basic morphological operator's dilation, erosion, opening and closing transform an indeterminate image into another indeterminate image. Therefore, in fact, we deal with neutro-dilation, neutro-erosion, neutro-opening and neutro-closing. For a determinate image (i.e. image with no indeterminacy), the classical morphological operators transform it also into a determinate image, while the neutro-morphological operators into an indeterminate image. All work from below is available for both the indeterminate and determinate image. [ABSTRACT FROM AUTHOR]

The rapid development of renewable energy has also had an impact on the flexibility of multi energy systems such as electricity, heat, and gas. To analyze the flexible characteristics of multi energy systems at multiple time scales, a multi-scale flexibility evaluation method based on morphological decomposition is proposed. The net load curve is decomposed using mathematical morphology methods, and a multi-scale energy storage configuration method based on the flexibility of electric heating systems is proposed. The analysis data shows that the probability of insufficient upward flexibility, margin expectation, and insufficient expectation of the scale weighted flexibility index are 1.12%, 3.98%, and 1.16%, respectively, while the probability of insufficient downward flexibility, margin expectation, and insufficient expectation are 0.73%, 4.54%, and 0.56%, respectively. The introduction of energy storage and controllable load simultaneously results in an overall downward flexibility index of 0.92% for the system. The results indicate that controllable load can improve the economy of system peak shaving, providing more options for energy storage and configuration in multi energy systems. [ABSTRACT FROM AUTHOR]

This paper proposes a new, fully automatic and robust approach to generating triangular meshes directly from volumetric data (scanned images), particularly when these images contain multiple adjacent labels. Current meshing techniques produce a number of mesh elements directly related to the number of components (voxels) in the image. This number can be considerable if the image is large. The proposed methodology is significantly less dense in terms of the number of elements compared to marching cube methods. The proposed method presents no configuration ambiguity and is faithful to the original morphology of the images regardless of the thinness of the topologies or the presence of possible erratic morphological configurations that may lead to geometric interpretation indecisions. [Display omitted] • New fully automatic and robust approach to generate triangular meshes directly from volumetric data, in particular when these images contain adjoining multiple-label. • The proposed methodology is significantly less dense in terms of the number of elements compared to Marching Cube Methods. • The proposed method presents no configuration ambiguity and is faithful to the original morphology of the images regardless of the thinness of the topologies or the presence of possible erratic morphological configurations that may lead to geometric interpretation indecisions. [ABSTRACT FROM AUTHOR]

COVID‐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. [ABSTRACT FROM AUTHOR]

Thermal images are widely used for various applications such as safety, surveillance, and Advanced Driver Assistance Systems (ADAS). However, these images typically have low contrast, blurred aspect, and low resolution, making it difficult to detect distant and small-sized objects. To address these issues, this paper explores various preprocessing algorithms to improve the performance of already trained object detection networks. Specifically, mathematical morphology is used to favor the detection of small bright objects, while deblurring and super-resolution techniques are employed to enhance the image quality. The Logarithmic Image Processing (LIP) framework is chosen to perform mathematical morphology, as it is consistent with the Human Visual System. The efficacy of the proposed algorithms is evaluated on the FLIR dataset, with a sub-base focused on images containing distant objects. The mean Average-Precision (mAP) score is computed to objectively evaluate the results, showing a significant improvement in the detection of small objects in thermal images using CNNs such as YOLOv4 and EfficientDet. [ABSTRACT FROM AUTHOR]

The structuring element (SE) is a key component of any mathematical morphology (MM) operation and application dependent. However, there are no clear guidelines for the size selection of the SE for denoising application in power quality issues. To solve this problem, voltage sag and swell signals corrupted by white noise and impulse noise are modeled with considerations of the magnitude and position of impulse noise as well as the fault depth. Detailed numerical simulations are carried out to explore the impact of the semicircular SE with a variety of lengths and heights on the performance of morphological opening–closing–closing–opening average (OCCO) filters. Based on simulation results and observations, the impacts of three different factors on the output signal-to-noise ratio (SNR) and the size of the semicircular SE are summarized. Some guidelines for the size selection of the semicircular SE are proposed, which comprehensively considered the denoising effect and processing speed. It will facilitate the rapid implementation of a high-performance morphological filter compared to trial and error. [ABSTRACT FROM AUTHOR]

With the development goal of a low-cost and low-carbon reserve market, this paper proposes a dynamic assessment method for day-ahead consumption service reserve demand considering the forecast error of uncertainty power. The iterative self-organizing data analysis techniques algorithm is adopted to cluster the historical actual power into typical scenarios. In addition, the online matching between the typical scenario and the day-ahead forecast power is conducted. In order to realize the hierarchical quantification of reserve demand, the reserve resources in the whole power system are classified according to their response time. Furthermore, the mathematical morphology filter based on the structural elements that are consistent with the response time of the hierarchical reserve resources is initially applied to decompose the historical forecast error of the matched scenarios. The simulation results verify that the proposed dynamic assessment effectively reduces the reserve cost on the basis of being able to cope with multi-time-scale power fluctuations. [ABSTRACT FROM AUTHOR]

Objective: To develop and validate convolutional neural network algorithms for automatic upper airway segmentation and minimum cross-sectional area (CSAmin) localisation in two-dimensional (2D) radiographic airway images. Materials and Methods: Two hundred and one 2D airway images acquired using cone-beam computed tomography (CBCT) scanning were randomly assigned to a test group (n = 161) to train artificial intelligence (AI) models and a validation group (n = 40) to evaluate the accuracy of AI processing. Four AI models, UNet18, UNet36, DeepLab50 and DeepLab101, were trained to automatically segment the upper airway 2D images in the test group. Precision, recall, Intersection over Union, the dice similarity coefficient and size difference were used to evaluate the performance of the AI-driven segmentation models. The CSAmin height in each image was manually determined using three-dimensional CBCT data. The nonlinear mathematical morphology technique was used to calculate the CSAmin level. Height errors were assessed to evaluate the CSAmin localisation accuracy in the validation group. The time consumed for airway segmentation and CSAmin localisation was compared between manual and AI processing methods. Results: The precision of all four segmentation models exceeded 90.0%. No significant differences were found in the accuracy of any AI models. The consistency of CSAmin localisation in specific segments between manual and AI processing was 0.944. AI processing was much more efficient than manual processing in terms of airway segmentation and CSAmin localisation. Conclusions: We successfully developed and validated a fully automatic AI-driven system for upper airway segmentation and CSAmin localisation using 2D radiographic airway images. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The precise display of blood vessel information for doctors is crucial. This is not only true for facilitating intravenous injections, but also for the diagnosis and analysis of diseases. Currently, infrared cameras can be used to capture images of superficial blood vessels. However, their imaging quality always has the problems of noises, breaks, and uneven vascular information. In order to overcome these problems, this paper proposes an image segmentation algorithm based on the background subtraction and improved mathematical morphology. The algorithm regards the image as a superposition of blood vessels into the background, removes the noise by calculating the size of connected domains, achieves uniform blood vessel width, and smooths edges that reflect the actual blood vessel state. The algorithm is evaluated subjectively and objectively in this paper to provide a basis for vascular image quality assessment. Extensive experimental results demonstrate that the proposed method can effectively extract accurate and clear vascular information. [ABSTRACT FROM AUTHOR]

The existing crack image segmentation methods cannot describe the characteristics of smaller cracks, which leads to incomplete crack extraction results in pavement images. Therefore, a pavement crack image segmentation method based on mathematical morphology is proposed. Use the spatial domain processing method to enhance the original image pixels, obtain a new pavement crack image through forward transformation and inverse transformation, and classify the noise type in the image; use the gray value to represent the noise, and use the 3× in the mean filter method 3 The template represents the crack element and noise element of the discrete image, and denoises the pavement crack image; uses the classical mathematical morphology theory to express the structural elements of the pavement crack image, and judges the form change of the crack in the process of expansion and corrosion processing, to achieve crack segmentation. The experimental results of single crack image and complex crack image show that the proposed method can effectively extract the crack feature value, the feature extraction rate is 99.5%, and it has high segmentation efficiency under different noise conditions, which can meet the needs of pavement crack detection. [ABSTRACT FROM AUTHOR]

A pattern recognition-based plan for detecting islanding and non-islanding conditions is presented in this paper. This plan is used in distribution systems in the presence of synchronous generator-based distributed generation resources with doubly fed induction generators (DFIGs), a generating principle widely used in wind turbines. The main idea behind the suggested plan is the measurement of total magnetomotive force (MMF) in the SG air gap less than 2 s based on IEEE std.1547 after the occurrence of islanding and non-islanding events. First, a mathematical morphology (MM) filter was used to lessen the possible noise in a total MMF signal sampled in SGs. Then, spatial features like variation, energy, median, rms and mean values of the output magnitude of MM filter were computed. Finally, to distinguish the islanding condition from the non-islanding conditions, support vector machine (SVM) classifier with radial basis function (RBF) kernel was taught based on the above-mentioned features. Different scenarios were considered in various loading conditions in distribution systems for the occurrence of islanding and non-islanding conditions and the determination of the ability of the suggested plan. The outcomes show the method suggested with the SVM (RBF kernel) classifier has much better results in comparison with some other methods. [ABSTRACT FROM AUTHOR]

Monitoring of high-density images from video sequences provides an important potential for crowd detection and classification. In fact, shadow presence from video sequences causes detection failures of results or mistakes in interpretation. In this contribution, we present an automatic system to deal with shadow elimination based on extracting the vector size of the movement and detection of occlusion management with the Fourier series approach because of the position and orientation of the camera, speed magnitude and visual tracking of crowd scenes, and mathematical morphology of discrete data in a non-linear approach. The model consists of distinctive real objects from fused data and crowded scene caused by shades, which often has consequence such as the failure counting and grading of vehicle and people, while dealing with traditional methods. As we reveal, our technique is principally appropriate for UMN and PETS data to eliminate shadow nuisance and detection occlusion with quite good performance. For classification, we use two classes in each image, for each category of events detected by fuzzy logic. Although this comes down to easy system modeling, as it relates to the use of fuzzy rules. The provided results advocate in favor of our method in terms of effectiveness and precision. Indeed, the proposed approach provides the ability to segment more specific objects, such as people and vehicles in real-time space. The results were compared to other methods, namely Covariance Matrices for Crowd Behavior, Social Force Method, and Ground Truth Technique. [ABSTRACT FROM AUTHOR]

Recurring morphologies observed in various migrating cell types are associated with the directedness and exploratory nature. This article introduces mathematical representation of the morphology dynamics and means by which the simulation results can be systematically compared with microscopy data. [ABSTRACT FROM AUTHOR]

Background: The removal of hair and ruler marks is critical in handcrafted image analysis of dermoscopic skin lesions. No other dermoscopic artifacts cause more problems in segmentation and structure detection. Purpose: The aim of the work is to detect both white and black hair, artifacts and finally inpaint correctly the image. Method: We introduce a new algorithm: SharpRazor, to detect hair and ruler marks and remove them from the image. Our multiple‐filter approach detects hairs of varying widths within varying backgrounds, while avoiding detection of vessels and bubbles. The proposed algorithm utilizes grayscale plane modification, hair enhancement, segmentation using tri‐directional gradients, and multiple filters for hair of varying widths. We develop an alternate entropy‐based processing adaptive thresholding method. White or light‐colored hair, and ruler marks are detected separately and added to the final hair mask. A classifier removes noise objects. Finally, a new technique of inpainting is presented, and this is utilized to remove the detected object from the lesion image. Results: The proposed algorithm is tested on two datasets, and compares with seven existing methods measuring accuracy, precision, recall, dice, and Jaccard scores. SharpRazor is shown to outperform existing methods. Conclusion: The Shaprazor techniques show the promise to reach the purpose of removing and inpaint both dark and white hair in a wide variety of lesions. [ABSTRACT FROM AUTHOR]

Radiologists use mammogram images for the diagnosis of breast cancer. However, interpreting these images remains challenging depending on the type of breast, especially on dense breasts. Dense breasts may contain abnormal structures similar to normal breast tissue and could lead to a high rate of false positives and false positives negatives. We present an efficient computer-aided diagnostic system for detecting and classifying breast masses. After removing noise and artifacts from the images using 2D median filtering, mathematical morphology and pectoral muscle removal by Hough's algorithm, the resulting image is used for breast mass segmentation using the watershed algorithm. After the segmentation, the system extracts several data by the wavelet transform and the cooccurrence matrix (GLCM) to finally lead to a classification as malign or benign mass via the Support Vector Machine (SVM) classifier. This method was applied to 48 Medio-Lateral Oblique (MLO) images from the image base (mini-MIAS). The algorithm showed a 87.5% classification rate, 92.59% sensitivity, and a specificity of 93.94%. [ABSTRACT FROM AUTHOR]

During the long-term storage of radioactive waste, the continuous generation of gas in the disposal area may influence the integrity of host rock. Thus, the investigation of gas migration and breakthrough in low-permeability rock is indispensable for the stability assessment. In this work, the pore space models of four potential host rocks (Boom clay, COx argillite, Opalinus clay, and Beishan granite) were generated via the binarization of the Gaussian random field. This method provides a randomly formed pore network that does rely on an initial definition of pore shape. The constructed models were analyzed and validated by using the mathematical morphology. A numerical calculation scenario of gas breakthrough on the basis of the Young–Laplace equation was proposed and applied. Results show that the gas breakthrough pressures are 2.62–4.11 MPa in Boom clay and 3.72–4.27 MPa in COx argillite. It enhances the idea that the capillary-induced gas breakthrough is possible at pressures lower than the fracture threshold. For Opalinus clay and Beishan granite, no connected pathway exists, and the breakthrough is more likely to occur through pathway dilation or fractures. The presented method has the advantage of experimental reproducibility and brings a new idea for the investigation of fluid migration in low-permeability rocks. [ABSTRACT FROM AUTHOR]

Hexagonal grid layouts are advantageous in microarray technology; however, hexagonal grids appear in many fields, especially given the rise of new nanostructures and metamaterials, leading to the need for image analysis on such structures. This work proposes a shock-filter-based approach driven by mathematical morphology for the segmentation of image objects disposed in a hexagonal grid. The original image is decomposed into a pair of rectangular grids, such that their superposition generates the initial image. Within each rectangular grid, the shock-filters are once again used to confine the foreground information for each image object into an area of interest. The proposed methodology was successfully applied for microarray spot segmentation, whereas its character of generality is underlined by the segmentation results obtained for two other types of hexagonal grid layouts. Considering the segmentation accuracy through specific quality measures for microarray images, such as the mean absolute error and the coefficient of variation, high correlations of our computed spot intensity features with the annotated reference values were found, indicating the reliability of the proposed approach. Moreover, taking into account that the shock-filter PDE formalism is targeting the one-dimensional luminance profile function, the computational complexity to determine the grid is minimized. The order of growth for the computational complexity of our approach is at least one order of magnitude lower when compared with state-of-the-art microarray segmentation approaches, ranging from classical to machine learning ones. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for clutter removal in airborne radars using a genetic algorithm and mathematical morphology. The clutter returns are detected when constant alarm rate processing is applied on range-Doppler images. In the proposed method, mathematical morphological operations are performed on range-Doppler images to obtain clutter images. The clutter image is then applied as a mask to remove false detections due to clutter. Also, the targets embedded in clutter are detected using gray-scale morphological operations. The morphological filter and the sequence of operations are designed by a genetic algorithm. The advantage of the proposed method is that it does not require the computation of statistical measures from clutter data and filters are optimally designed using a genetic algorithm. The proposed method has shown an increase in clutter leak reduction when compared to that of a deep morphological network. [ABSTRACT FROM AUTHOR]

With the rapid growth of China's economy and the supportive government policies and funds, the demand for and the expansion of rural housing in China are on the rise, which tightens requirements for rural housing. However, most of the present rural housing suffers from unplanned growth. The sprawl of rural housing adversely affects the quantity and quality of land resources, in particular, productive agricultural lands; therefore, it is necessary to regulate the growth of rural housing and protect farmlands through spatiotemporally continuous monitoring. Currently, the monitoring of rural housing in China is mainly conducted via in-situ inspection of the national land survey, which is restricted by unfavorable conditions (e.g., weather, outbreaks, and traffic) as well as impairing real-time and reliable control over information collected. To resolve this issue, this study proposed an intelligent model to recognize rural housing in underdeveloped areas based on deep learning and mathematical morphology (MobileNet-MM). The model was based on high-resolution remote sensing data, MobileNetV2 (a convolutional neural network architecture well performing on mobile devices), and mathematical morphology. First, the obtained data were segmented and manually screened and tagged to construct a training dataset. Second, the training dataset was used to train MobileNet-MM, with the expansion operation being used to compensate for identification errors of deep learning. Finally, the accuracy of MobileNet-MM to identify and monitor rural housing was tested, resulting in 84.5% accuracy. The comparison of the accuracies of MobileNet-MM and ResNet34 (a state-of-the-art image classification model) indicated that ResNet34 misclassified a large area of rural housing that was mainly distributed on the edge of the region as well as cropland and vegetation as rural housing, with its weak ability to recognize actual rural housing. The MobileNet-MM model predicted rural housing accurately and land boundary precisely, with the misclassified area being scattered, and its average accuracy, is 10.6% higher than that of ResNet34. The novelties of this study were two-fold: (1) a high-resolution training dataset of rural housing in underdeveloped areas was generated, which provides data support for the development of subsequent models; and (2) an intelligent model to recognize rural housing in underdeveloped areas (MobileNet-MM) based on deep learning and mathematical morphology was proposed. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy is a retinal compilation that causes visual impairment. Hemorrhage is one of the pathological symptoms of diabetic retinopathy that emerges during disease development. Therefore, hemorrhage detection reveals the presence of diabetic retinopathy in the early phase. Diagnosing the disease in its initial stage is crucial to adopt proper treatment so the repercussions can be prevented. The automatic deep learning-based hemorrhage detection method is proposed that can be used as the second interpreter for ophthalmologists to reduce the time and complexity of conventional screening methods. The quality of the images was enhanced, and the prospective hemorrhage locations were estimated in the preprocessing stage. Modified gamma correction adaptively illuminates fundus images by using gradient information to address the nonuniform brightness levels of images. The algorithm estimated the locations of potential candidates by using a Gaussian match filter, entropy thresholding, and mathematical morphology. The required objects were segmented using the regional diversity at estimated locations. The novel hemorrhage network is propounded for hemorrhage classification and compared with the renowned deep models. Two datasets benchmarked the model's performance using sensitivity, specificity, precision, and accuracy metrics. Despite being the shallowest network, the proposed network marked competitive results than LeNet-5, AlexNet, ResNet50, and VGG-16. The hemorrhage network was assessed using training time and classification accuracy through synthetic experimentation. Results showed promising accuracy in the classification stage while significantly reducing training time. The research concluded that increasing deep network layers does not guarantee good results but rather increases training time. The suitable architecture of a deep model and its appropriate parameters are critical for obtaining excellent outcomes. [ABSTRACT FROM AUTHOR]

We presented an operational rationale grounded on complex system thinking to quantify structural and functional landscape transformations along three stages representative of post-war metropolitan development in Rome, Italy (urbanisation with population/settlement densification, 1949–1974; suburbanisation with medium-density settlement expansion, 1974–1999; counter-urbanisation with settlement sprawl, 1999–2016). A mathematical morphology approach assessing the geometric form of land patches and a multi-way factor analysis (MFA) of landscape metrics were used to investigate the joint evolution of urban form and land-use functions over time. The empirical results of the MFA delineated the multivariate relationship between nine land-use classes (with distinctive socioeconomic functions) and seven morphological types (reflecting different landscape structures) according to four observation times (1949, 1974, 1999, 2016). Taken as an intrinsic attribute of complex landscape systems experiencing intense transformations, an estimation of the 'rapidity-of-change' in the form-functions relationship at a given development stage was derived from MFA outcomes separately for urbanisation, suburbanisation, and counter-urbanisation. A simplified form-functions relationship, reflecting the spatial polarisation in compact settlements and rural (low-density) landscapes, was observed with compact urbanisation. By stimulating urban sprawl into fringe farmland, suburbanisation resulted in patchy and heterogeneous rural landscapes. Counter-urbanization was associated with the fragmentation of built-up settlements leading to a chaotic mosaic of land structures that mixes urban and rural traits. Rapidity-of-change in form-function relationships was greater during suburbanisation than urbanisation and counter-urbanisation. It reflects the intrinsic pressure of economic growth in contemporary cities. [ABSTRACT FROM AUTHOR]

Observing the structural dynamics of biomolecules is vital to deepening our understanding of biomolecular functions. High-speed (HS) atomic force microscopy (AFM) is a powerful method to measure biomolecular behavior at near physiological conditions. In the AFM, measured image profiles on a molecular surface are distorted by the tip shape through the interactions between the tip and molecule. Once the tip shape is known, AFM images can be approximately deconvolved to reconstruct the surface geometry of the sample molecule. Thus, knowing the correct tip shape is an important issue in the AFM image analysis. The blind tip reconstruction (BTR) method developed by Villarrubia (J Res Natl Inst Stand Technol 102:425, 1997) is an algorithm that estimates tip shape only from AFM images using mathematical morphology operators. While the BTR works perfectly for noise-free AFM images, the algorithm is susceptible to noise. To overcome this issue, we here propose an alternative BTR method, called end-to-end differentiable BTR, based on a modern machine learning approach. In the method, we introduce a loss function including a regularization term to prevent overfitting to noise, and the tip shape is optimized with automatic differentiation and backpropagations developed in deep learning frameworks. Using noisy pseudo-AFM images of myosin V motor domain as test cases, we show that our end-to-end differentiable BTR is robust against noise in AFM images. The method can also detect a double-tip shape and deconvolve doubled molecular images. Finally, application to real HS-AFM data of myosin V walking on an actin filament shows that the method can reconstruct the accurate surface geometry of actomyosin consistent with the structural model. Our method serves as a general post-processing for reconstructing hidden molecular surfaces from any AFM images. Codes are available at https://github.com/matsunagalab/differentiable_BTR. [ABSTRACT FROM AUTHOR]

Numerous methods based on square rasters have been proposed for polygon generalization. However, these methods ignore the inconsistent distance measurement among neighborhoods of squares, which may result in an imbalanced generalization in different directions. As an alternative raster, a hexagon has consistent connectivity and isotropic neighborhoods. This study proposed a hexagon-based method for polygon generalization using morphological operators. First, we defined three generalization operators: aggregation, elimination, and line simplification, based on hexagonal morphological operations. We then used corrective operations with selection, skeleton, and exaggeration to detect, classify, and correct the unreasonably reduced narrow parts of the polygons. To assess the effectiveness of the proposed method, we conducted experiments comparing the hexagonal raster to square raster and vector data. Unlike vector-based methods in which various algorithms simplified either areal objects or exterior boundaries, the hexagon-based method performed both simplifications simultaneously. Compared to the square-based method, the results of the hexagon-based method were more balanced in all neighborhood directions, matched better with the original polygons, and had smoother simplified boundaries. Moreover, it performed with shorter running time than the square-based method, where the minimal time difference was less than 1 min, and the maximal time difference reached more than 50 mins. [ABSTRACT FROM AUTHOR]

In the development of modern tools, the flute (or profiling flute) after design optimization is often obtained by grooving with a forming grinding wheel. At present, the main method of reverse forming wheel profile is the analytical method, which needs to solve the contact line equation according to the contact conditions. It is difficult to solve the equation. The solution value is unstable, which leads to the design error of the grinding wheel profile. In order to solve this problem, this paper proposes a new algorithm, called the pixel matrix method for short. This method is based on the spiral motion envelope method and mathematical morphology. First, the cross-section of the flute is discretized into a point cloud, and then, the envelope motion is carried out in the grinding wheel coordinate system. Second, the point cloud of the grinding wheel radial section is collected and converted into a binary image of pixel points. Finally, the profile of the binary image is extracted by erosion and dilation. The optimized profile of the formed grinding wheel reaches the accuracy requirements of the design and processing in the actual machining verification. This method can accurately reverse the profile of the forming grinding wheel. The calculation process is intuitive, avoiding the solution of the contact line equation, and the solution value is stable. It provides a new way to reverse the profile of the machining tool for cylindrical spiral products. [ABSTRACT FROM AUTHOR]

Change detection using synthetic aperture radar (SAR) multi-temporal images only detects the change area and generates no information such as change type, which limits its development. This study proposed a new unsupervised application of SAR images that can recognize the change type of the area. First, a regionally restricted principal component analysis k-mean (RRPCA-Kmean) clustering algorithm, combining principal component analysis, k-mean clustering, and mathematical morphology composition, was designed to obtain pre-classification results in combination with change type vectors. Second, a lightweight MobileNet was designed based on the results of the first stage to perform the reclassification of the pre-classification results and obtain the change recognition results of the changed regions. The experimental results using SAR datasets with different resolutions show that the method can guarantee change recognition results with good change detection correctness. [ABSTRACT FROM AUTHOR]

We discuss a full‐scale treatment of real‐life Raman spectra with pronounced artifacts via mathematical morphology: Baseline correction, peak recognition, and cosmic ray removal are provided using a single kernel. The suggested treatment is peak‐oriented, and its novelty lies in utilization of contact points between a spectrum and its morphological mapping to provide robust peak recognition via low‐order morphological operators. Reaching a compromise between complicated iterative processing and requiring tedious input from the user, the provided algorithms permit effective unsupervised evaluation of individual spectra as well as Raman maps and time or depth series. The method is demonstrated on SERS identification of selected designer drugs. [ABSTRACT FROM AUTHOR]

Detection and classification with traditional neural networks methods such as multilayer perceptron (MLP), feed forward network and back propagation neural networks show several drawbacks including the rate of convergence and the incapacity facing the problems of size of the image especially for radar images. As a result, these methods are being replaced by other evolutional classification methods such as Higher Order Neural Networks (HONN) (Functional Link Artificial Neural Network (FLANN), Pi Sigma Neural Network (PSNN), Neural Network Product Unit (PUNN) and Neural Network of the Higher Order Processing Unit. So, in this paper, we address radar object detection and classification problems with a new strategy by using PSNN and a new proposed method HOGE for edges features extraction based on morphological operators and histogram of oriented gradient. Thus, in order to recognise radar object, we extract HOG features of the object region and classify our target with PSNN. The HOGE features vector is used as input of pi-sigma NN. The proposed method was tested and confirmed based on experiments through the use of 2D and 3D ISAR images. [ABSTRACT FROM AUTHOR]

Overlap and grouping functions are important aggregation operators, especially in information fusion, classification and decision-making problems. However, when we do more in-depth application research (for example, non-commutative fuzzy reasoning, complex multi-attribute decision making and image processing), we find overlap functions as well as grouping functions are required to be commutative (or symmetric), which limit their wide applications. For the above reasons, this paper expands the original notions of overlap functions and grouping functions, and the new concepts of pseudo overlap functions and pseudo grouping functions are proposed on the basis of removing the commutativity of the original functions. Some examples and construction methods of pseudo overlap functions and pseudo grouping functions are presented, and the residuated implication (co-implication) operators derived from them are investigated. Not only that, some applications of pseudo overlap (grouping) functions in multi-attribute (group) decision-making, fuzzy mathematical morphology and image processing are discussed. Experimental results show that, in many application fields, pseudo overlap functions and pseudo grouping functions have greater flexibility and practicability. [ABSTRACT FROM AUTHOR]

An accurate earthquake-induced damage assessment from very high resolution satellite images requires a joint use of spectral and spatial information. The spatial relations between pixels in damaged areas should properly be included in the classification model in order to exploit the spatial information. Morphological Profiles (MPs) and Attribute Profiles (APs) are the state-of-the-art image processing methods that produce a set of filtered images, which are able to highlight specific patterns representing a specific land cover class. In this study, the MPs and the APs are used to create additional spatial features for two different very high resolution post-event satellite images, acquired from the City of Bam in Iran and the City of Port-au-Prince in Haiti. These contextual features are then analyzed by means of a feature selection algorithm, called Minimum Redundancy Maximum Relevance, to find the most relevant features contributing the damage class the most. A final feature subset of selected features is analyzed using two different classifiers, which are k-nearest neighbors and support-vector machines. The results show that the use of a proper configuration of those profiles can significantly improve the classification accuracy and the quality of the thematic map, but generalization of the classification model is limited especially for the larger areas. [ABSTRACT FROM AUTHOR]