An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

Mathematical Morphology View of Topological Rough Sets and Its Applications

This article focuses on the relationship between mathematical morphology operations and rough sets,mainly based on the context of image retrieval and the basic image correspondence problem.Mathematical morphological procedures and set approximations in rough set theory have some clear parallels.Numerous initiatives have been made to connect rough sets with mathematical morphology.Numerous significant publications have been written in this field.Others attempt to show a direct connection between mathematical morphology and rough sets through relations,a pair of dual operations,and neighborhood systems.Rough sets are used to suggest a strategy to approximatemathematicalmorphology within the general paradigm of soft computing.A single framework is defined using a different technique that incorporates the key ideas of both rough sets and mathematical morphology.This paper examines rough set theory from the viewpoint of mathematical morphology to derive rough forms of themorphological structures of dilation,erosion,opening,and closing.These newly defined structures are applied to develop algorithm for the differential analysis of chest X-ray images from a COVID-19 patient with acute pneumonia and a health subject.The algorithm and rough morphological operations show promise for the delineation of lung occlusion in COVID-19 patients from chest X-rays.The foundations of mathematical morphology are covered in this article.After that,rough set theory ideas are taken into account,and their connections are examined.Finally,a suggested image retrieval application of the concepts from these two fields is provided.

Skeleton extraction of boiler flame images based on mathematical morphology

In this paper, a method and algorithm of skeleton extraction based on binary mathematical morphology is presented. Sequential structuring elements (SEs) is also studied, which is the key problem of skeleton extraction. The examples of boiler flame image processing show that the detected skeletons can present the geometric shape of flame images well.

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.

Void defect detection in BGA solder joints using mathematical morphology

Voids are one of the major defects in ball grid array （BGA） solder joints due to a large amount of outgassing flux that gets entrapped during reflow. X-ray nondestructive machines are used to make voids visible as lighter areas inside the solder joints in X-ray images for detection However, it has always been difficult to analyze this problem automatically because of some challenges such as noise, inconsistent lighting and void-like artifacts. This study realized accurate extraction and automatic a-nalysis of void defects in solder joints by adopting a technical proposal, in which Otsu algorithm was used to segment solder balls and void defects were extracted through opening and closing operations and top-hat transformation in mathematical mor-phology. Experimental results show that the technical proposal mentioned here has good robustness and can be applied in the detection of voids in BGA solder joints.

Pseudo-Semi-Overlap Functions-Based Fuzzy Rough Sets Applied to Image Edge Extraction

As an extension of overlap functions, pseudo-semi-overlap functions are a crucial class of aggregation functions. Therefore, (I, PSO)-fuzzy rough sets are introduced, utilizing pseudo-semi-overlap functions, and further extended for applications in image edge extraction. Firstly, a new clustering function, the pseudo-semi-overlap function, is introduced by eliminating the symmetry and right continuity present in the overlap function. The relaxed nature of this function enhances its applicability in image edge extraction. Secondly, the definitions of (I, PSO)-fuzzy rough sets are provided, using (I, PSO)-fuzzy rough sets, a pair of new fuzzy mathematical morphological operators (IPSOFMM operators) is proposed. Finally, by combining the fuzzy C-means algorithm and IPSOFMM operators, a novel image edge extraction algorithm (FCM-IPSO algorithm) is proposed and implemented. Compared to existing algorithms, the FCM-IPSO algorithm exhibits more image edges and a 73.81% decrease in the noise introduction rate. The outstanding performance of (I, PSO)-fuzzy rough sets in image edge extraction demonstrates their practical application value.

Image edge detection based on nonsubsampled contourlet transform and mathematical morphology

A novel algorithm for image edge detection is presented. This algorithm combines the nonsubsampled contourlet transform and the mathematical morphology. First, the source image is decomposed by the nonsubsampled contourlet transform into multi-scale and multi-directional subbands. Then the edges in the high-frequency and low-frequency sub-bands are respectively extracted by the dualthreshold modulus maxima method and the mathematical morphology operator. Finally, the edges from the high- frequency and low-frequency sub-bands are integrated to the edges of the source image, which are refined, and isolated points are excluded to achieve the edges of the source image. The simulation results show that the proposed algorithm can effectively suppress noise, eliminate pseudo-edges and overcome the adverse effects caused by uneven illumination to a certain extent. Compared with the traditional methods such as LoG, Sobel, and Carmy operators and the modulus maxima algorithm, the proposed method can maintain sufficient positioning accuracy and edge details, and it can also make an improvement in the completeness, smoothness and clearness of the outline.

Quantitative analysis of intrusive body morphology and its relationship with skarn mineralization——A case study of Fenghuangshan copper deposit, Tongling, Anhui, China

The shapes of intrusive body and contact zone might influence the formation and distribution of orebodies in skarn deposit.By taking Xinwuli intrusive body in Fenghuangshan copper deposit,Tongling,Anhui,China,as the research object,a new method was used to obtain the quantitative relationship between intrusion morphology and skarn mineralization.The first step of the method was to extract morphological characteristic parameters based on mathematical morphology and Euclidean distance transformation;then the quantitative relationship between the parameters and orebodies was analyzed;finally correlational analyses between the parameters and mineralization indices were conducted.The results show that morphological characteristic parameters can effectively indicate the location of concealed ore bodies in skarn deposit,with the following parts as advantageous positions of skarn mineralization:(1)the parts away from the1st trend surface in the range from?25to50m;(2)the convex parts about200m away from the2nd trend surface,around which the tangent plane of the intrusive body is approximately consistent with the trend surface;(3)the contact zones with angle between intrusive body original contact surface and trend contact surface ranging from35°to70°;(4)the parts with angle between intrusive body original contact surface and regional extruding far crustal stress ranging from50°to60°.These knowledge can be applied to more skarn deposits for future mineral exploration.

EDGE DETECTION METHOD OF REMOTE SENSING IMAGES BASED ON MATHEMATICAL MORPHOLOGY OF MULTI-STRUCTURE ELEMENTS

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise & remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.

Quantitative Study on Polymer Flocculation Mechanism Based on Mathematical Morphology

Until now, understanding of polymer flocculation has remained restricted within the qualitative explanations of the bridge unite theory and the electricity neutralization theory, because people not only lacked the systemic knowl- edge of the polymer flocculation mechanism, the flocculation dynamic process study and the flocculation effect esti- mate, but also could not penetrate within the flocculation process microscopic field to obtain the structural character parameters such as floccule structure, the frame bridge models and so on. In this paper, not only coal slurry flocculation images were photographed by using the transmission electron microscope, but also the basic theory of the mathematical morphology was applied to the coal slurry flocculation image processing. The steps and methods of the mathematical morphology were expounded in detail. The micro-structural parameters such as the flocculate size and the bridge length were obtained, which combined the microscopic flocculation grain configuration observations with the macroscopic flocculation effect, so as to get the maximum amount of veracious information to describe and explain the whole floc- culation course by rule and line. On this basis, not only the flocculation models of polymers in the coal slurry are sug- gested, but the quantitative study on flocculation mechanism has been achieved.

Extracting heartrate from optical signal of functional near-infrared spectroscopy based on mathematical morphology

Functional near-infrared spectroscopy(fNIRS),as a new optical functional neuroimaging method,has been widely used in neuroscience research.In some research fields with NIRS,heartrate(HR)(or heartbeat)is needed as useful information to evaluate its influence,or to know the state ofsubject,or to remove its artifact.If HR(or heartbeat)can be detected with high accuracy from theoptical intensity,this will undoubtedly benefit a lot to many NIRS studies.Previous studies haveused the moving time window method or mathematical morphology method(MMM)to detectheartbeats in the optical intensity.However,there are some disadvantages in these methods.In thisstudy,we proposed a method combining the periodic information of heartbeats and the operator ofmathematical morphology to automatically detect heartbeats in the optical intensity.First theoptical intensity is smoothed using a moving average flter.Then,the opening operator of math-ematical morphology extracts peaks in the smoothed optical intensity.Finally,one peak is iden-tified as a heartbeat peak if this peak is the maximum in a predefined point range.Throughvalidation on experimental data,our method can overcome the disadvantages of previous methods,and detet heartbeats in the optical signal of fNIRS with nearly 100%accuracy.

Speckle Suppress for SAR Images Based on Pyramidal Morphology Algorithm

A Pyramidal Morphology Algorithm is developed for speckle reduction of SARimages in this paper. For reducing the loss of information in the pyramidal algorithm for morphologyprocessing, in this modified algorithm, the sub-images are processed parallel in the downsamplingoperation and the sub-images are reconstructed in the upsampling operation. It can be applied toimage filtering parallel. After analysis the computer simulations show that these two kinds offilters are both effective in speckle reduction of SAR images. The modified parallel algorithm doesbetter than the original algorithm and Lee filter on some characteristics.

A Smart Mathematic Morphology Method for Recognition and Understanding of Airfield

A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structure features of airfield, a segmentation method called recursive Otsu algorithm is employed on an airfield image. Second, thinning and shrinking algorithms are utilized to obtain the contour of airfield with single pixel and to remove diffused small particles. Finally, Radon transform is adopted to extract two typical and important components, primary and secondary runways of airfield exactly. At the same time, region growing algorithm is exploited to get the other components such as parking apron and garages. The experimental results demonstrate that the proposed method gives good performance.

Mathematical Morphology-Based Artificial Technique for Renewable Power Application

This paper suggests a combined novel control strategy for DFIG based wind power systems(WPS)under both nonlinear and unbalanced load conditions.The combined control approach is designed by coordinating the machine side converter(MSC)and the load side converter(LSC)control approaches.The proposed MSC control approach is designed by using a model predictive control(MPC)approach to generate appropriate real and reactive power.The MSC controller selects an appropriate rotor voltage vector by using a minimized optimization cost function for the converter operation.It shows its superiority by eliminating the requirement of transformation,switching table,and the PWM techniques.The proposed MSC reduces the cost,complexity,and computational burden of the WPS.On the other hand,the LSC control approach is designed by using a mathematical morphological technique(MMT)for appropriate DC component extraction.Due to the appropriate DC-component extraction,the WPS can compensate the harmonics during both steady and dynamic states.Further,the LSC controller also provides active power filter operation even under the shutdown of WPS condition.To verify the applicability of coordinated control operation,the WPS-based microgrid system is tested under various test conditions.The proposed WPS is designed by using a MATLAB/Simulink software.

Analysis of quantitative pore features based on mathematical morphology

Wood identification is a basic technique of wood science and industry. Pore features are among the most important identification features for hardwoods. We have used a method based on an analysis of quantitative pore feature, which differs from traditional qualitative methods. We applies mathematical morphology methods such as dilation and erosion, open and close transforma- tion of wood cross-sections, image repairing, noise filtering and edge detection to segment the pores from their background. Then the mean square errors （MSE） of pores were computed to describe the distribution of pores. Our experiment shows that it is easy to classify the pore features into three basic types, just as in traditional qualitative methods, but with the use of MSE of pores. This quantitative method improves wood identification considerably.

Image mathematical morphology and image restoration application in detecting underground bin level

By using image recognition technology, the underground bin level can be detdcted. The bin image is noised by vibration, atomy, backgroun and so on. The image restoration and image mathematical morphology were used based on neural network. A modified Hopfield network was presented for image restoration. The greed algorithm with n-simultaneous updates and apartially asynchronous algorithm were combined, im- proving convergence and avoiding synchronization penalties. Mathematical morphology was widely applicated in digital image processing. The basic idea of mathematical mor- phology is to use construction element measure image morphology for solving under- stand problem. Presented advanced Cellular neural network that forms MMCNN equa- tion to be suit for mathematical morphology filter. It gave the theory of MMCNN dynamic extent and stable state. It was evidenced that arrived mathematical morphology filter through steady of dynamic precess in definite condition. The results of implementation were applied in detecting undergroug bin level.

Gear Vibration Analysis and Gear Fault Diagnosis based on Mathematical Morphology

Gear vibration analysis and gear fault diagnosis are related to the multi-objective decision-making process of machinery equipment production, in which a large amount of data and information should be collected, and the relationship between supply/demand needs and available resources, between production and labor, and between enterprise benefit and social benefit should be balanced generally. Thus, the gear fault diagnosis technologies as well as the professional quality and technical quality are required to be very high. To conform to the forward development of mathematical modeling technology, it is urgent to implement safety product management with computer by using gear vibration analysis and gear fault diagnosis as methods for aiding the research and development of machinery gear fault diagnosis system. 7

A Hybrid Signal Processing Method Combining Mathematical Morphology and Walsh Theory for Power Quality Disturbance Detection and Classification

In this paper, a novel signal processing method combining mathematical morphology (MM) and Walsh theory is proposed, which uses Walsh functions to control the structuring element (SE) and MM operators. Based on the Walsh-MM method, a scheme for power quality disturbances detection and classification is developed, which involves three steps: denoising, feature extraction and morphological clustering. First, various evolution rules of Walsh function are used to generate groups of SEs for the multiscale Walsh-ordered morphological operation, so the original signal can be denoised. Next, the fundamental wave of the denoised signal is suppressed by Hadamard matrix;thus, disturbances can be extracted. Finally, the Walsh power spectrum of the waveform extracted in the previous step is calculated, and the parameters of which are taken by morphological clustering to classify the disturbances. Simulation results reveal the proposed scheme can effectively detect and classify disturbances, and the Walsh-MM method is less affected by noise and only involves simple calculation, which has a potential to be implemented in hardware and more suitable for real-time application.

Experimental and Numerical Research on Water Transport during Adsorption and Desorption in Cement-Based Materials

The durability of cement-based materials is related to water transport and storage in their pore network under different humidity conditions.To understand the mechanism and characteristics of water adsorption and desorption processes from the microscopic scale,this study introduces different points of view for the pore space model generation and numerical simulation of water transport by considering the“ink-bottle”effect.On the basis of the pore structure parameters(i.e.,pore size distribution and porosity)of cement paste and mortar with water-binder ratios of 0.3,0.4 and 0.5 obtained via mercury intrusion porosimetry,randomly formed 3D pore space models are generated using two-phase transformation on Gaussian random fields and verified via image analysis method of mathematical morphology.Considering the Kelvin-Laplace equation and the influence of“ink-bottle”pores,two numerical calculation scenarios based on mathematical morphology are proposed and applied to the generated model to simulate the adsorption-desorption process.The simulated adsorption and desorption curves are close to those of the experiment,verifying the effectiveness of the developed model and methods.The obtained results characterize water transport in cement-based materials during the variation of relative humidity and further explain the hysteresis effect due to“ink-bottle”pores from the microscopic scale.

RESEARCH ON SATELLITE IMAGE PROCESSING AND RECOGNITION WITH PARALLEL ALGORITHM

Using the method of mathematical morphology,this paper fulfills filtration,segmentation and extraction of morphological features of the satellite cloud image.It also gives out the relative algorithms,which is realized by parallel C programming based on Transputer networks.It has been successfully used to process the typhoon and the low tornado cloud image.And it will be used in weather forecast.