Optimal Segmentation Threshold Research Articles

Development of a Daily Cloud-Free Snow-Cover Dataset Using MODIS-Based Snow-Cover Probability for High Mountain Asia during 2000–2020

Investigating the changes in snow cover caused by climate change is extremely important and has attracted increasing attention in cryosphere and climate research. Optimal remote sensing-based snow datasets can provide long-term daily and global spatial-temporal snow-cover distribution at regional and global scales. However, the application of these snow-cover products is inevitably limited because of the space–time discontinuities caused by cloud obscuration, which poses a significant challenge in snowpack-related studies, especially in High Mountain Asia (HMA), an area that has high-elevation mountains, complex terrain, and harsh environments and has fewer observation stations. To address this issue, we developed an improved five-step hybrid cloud removal strategy by integrating the daily merged snow-cover probability (SCP) algorithm, eight-day merged SCP algorithm, decision tree algorithm, temporal downscaling algorithm, and optimal threshold segmentation algorithm to produce a 21-year, daily cloud-free snow-cover dataset using two daily MODIS snow-cover products over the HMA. The accuracy assessment demonstrated that the newly developed cloud-free snow-cover product achieved a mean overall accuracy of 93.80%, based on daily classified snow depth observations from 86 meteorological stations over 10 years. The time series of the daily percentage of binary snow-cover over HMA was analyzed during this period, indicating that the maximum snow cover tended to change more dramatically than the minimum snow cover. The annual snow-cover duration (SCD) experienced an insignificantly increasing trend over most of the northeastern and southwestern HMA (e.g., Qilian, eastern Kun Lun, the east of Inner Tibet, the western Himalayas, the central Himalayas, and the Hindu Kush) and an insignificant declining trend over most of the northwestern and southeastern HMA (e.g., the eastern Himalayas, Hengduan, the west of Inner Tibet, Pamir, Hissar Alay, and Tien). This new high-quality snow-cover dataset will promote studies on climate systems, hydrological modeling, and water resource management in this remote and cold region.

Detection of Alzheimer's disease using Otsu thresholding with tunicate swarm algorithm and deep belief network.

Introduction: Alzheimer's Disease (AD) is a degenerative brain disorder characterized by cognitive and memory dysfunctions. The early detection of AD is necessary to reduce the mortality rate through slowing down its progression. The prevention and detection of AD is the emerging research topic for many researchers. The structural Magnetic Resonance Imaging (sMRI) is an extensively used imaging technique in detection of AD, because it efficiently reflects the brain variations. Methods: Machine learning and deep learning models are widely applied on sMRI images for AD detection to accelerate the diagnosis process and to assist clinicians for timely treatment. In this article, an effective automated framework is implemented for early detection of AD. At first, the Region of Interest (RoI) is segmented from the acquired sMRI images by employing Otsu thresholding method with Tunicate Swarm Algorithm (TSA). The TSA finds the optimal segmentation threshold value for Otsu thresholding method. Then, the vectors are extracted from the RoI by applying Local Binary Pattern (LBP) and Local Directional Pattern variance (LDPv) descriptors. At last, the extracted vectors are passed to Deep Belief Networks (DBN) for image classification. Results and Discussion: The proposed framework achieves supreme classification accuracy of 99.80% and 99.92% on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker and Lifestyle flagship work of ageing (AIBL) datasets, which is higher than the conventional detection models.

Poststroke cognitive outcome is better accounted for by white matter abnormalities automated segmentation than visual analysis

Background and aimsThe association between white matter abnormalities (WMA) and cognitive decline previously reported in poststroke patients has been mainly documented using visual scales. However, automated segmentation of WMA provides a precise determination of the volume of WMA. Nonetheless, it is rarely used in the stroke population and its potential advantage over visual scales is still unsettled. The objective of this study was to examine whether automated segmentation of WMA provides a better account than the visual Fazekas and Wahlund scales of the decline in executive functions and processing speed in stroke patients. MethodsThe analyses were conducted on the 358 patients of the GRECogVASC cohort with an MRI performed at six months poststroke in the Amiens center. WMA were visually analyzed using the Fazekas (subcortical abnormalities) and Wahlund scales. Segmentation was performed using LST (3.0.3). Following preliminary studies to determine the optimal segmentation threshold, we examined the relationship between cognitive status and WMA volume computed at each threshold using receiver operating characteristic (ROC) curves. Finally, we assessed the ability of both Fazekas and Wahlund visual scores and WMA volume to account for cognitive scores by using a bivariate Pearson correlation analysis, comparing correlation coefficients with the Fisher transformation and repeating correlation analysis after adjustment for the lesion volume. ResultsIncreasing the threshold led to an underestimation of WMA (P=0.0001) (significant for a threshold ≥0.2) and an improvement in correct rejection of signal changes in the stroke cavity (P=0.02) (significant for a threshold ≤0.5), susceptibility artifacts (P=0.002) (significant for a threshold ≤0.6), and corticospinal degeneration (P=0.03) (significant for a threshold ≤0.5). WMA volume decreased with increasing threshold (P=0.0001). Areas under the curve (AUC) did not differ according to the threshold (processing speed: P=0.85, executive cognitive functions: P=0.7). Correlation coefficients between cognitive scores and WMA were higher for WMA volume than the Fazekas (processing speed: Z=−3.442, P=0.001; executive functions: Z=−2.751, P=0.006) and Wahlund scores (processing speed: Z=−3.615, P=0.0001; executive functions: Z=−2.769, P=0.006). Adjustment for lesion volume did not alter the correlations with WMA volume (processing speed: r=−0.327 [95%CI: −0.416; −0.223], P=0.0001; executive functions: r=−0.262 [95%CI: −0.363; −0.150], P=0.0001). ConclusionThis study shows that WMA volume assessed by automated segmentation provides a better account of cognitive disorders than visual analysis. This should favor its wider use to refine imaging determinants of poststroke cognitive disorders.

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

Features extraction of point clouds based on Otsu’s algorithm

Currently, a point cloud extraction method based on geometric features requires the configuration of two essential parameters: the neighborhood radius within the point cloud and the criterion for feature threshold selection. This article addresses the issue of manual selection of feature thresholds and proposes a feature extraction method for 3D point clouds based on the Otsu algorithm. Firstly, the curvature value of each point cloud is calculated based on the r-neighborhood of the point cloud data. Secondly, the Otsu algorithm is improved by taking the curvature values as input for the maximum inter-class variance method. The optimal segmentation threshold is obtained based on the Otsu algorithm to divide the point cloud data into two parts. Point cloud data whose curvature is greater than or equal to the threshold is extracted as feature point data. In order to verify the reliability of the algorithm presented in this paper, a method for accuracy assessment of regular point cloud data is proposed. Additionally, comparative analysis was conducted on data with varying point cloud densities and on data contaminated with Gaussian white noise using multiple methods. Experimental results show that the proposed algorithm achieves good extraction results for data with 90% simplification rate and low noise.

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Traditional artificial intelligence algorithms are prone to falling into local optima when solving threshold segmentation problems. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us solve the difficult problem. First, the algorithm methodology of an artificial Physarum polycephalum colony algorithm is described to search for the optimal solutions by expansion and contraction of a lot of artificial hyphae. Different artificial Physarum polycephalum can learn from each other and produce more hyphae in expansion. In contraction, the artificial Physarum polycephalum colony can select the best hyphae with high fitness through a quick sort algorithm, but the other hyphae with low fitness will be absorbed and disappear. Second, a fitness function is modeled based on Kapur’s entropy for the proposed artificial Physarum polycephalum colony algorithm to search for optimal threshold segmentation solutions. Third, a series of benchmark experiments are implemented to test the proposed artificial Physarum polycephalum colony algorithm, and some state-of-the-art approaches are employed for comparison. The experimental results verified that the proposed algorithm can obtain better accuracy and convergence speed, and is not easier to fall into the local optimal solution too early.

Optimization of Flow Imaging Microscopy Setting Using Spherical Beads with Optical Properties Similar to Those of Biopharmaceuticals

Flow imaging microscopy (FIM) is widely used to characterize biopharmaceutical subvisible particles (SVPs). The segmentation threshold, which defines the boundary between the particle and the background based on pixel intensity, should be properly set for accurate SVP quantification. However, segmentation thresholds are often subjectively and empirically set, potentially leading to variations in measurements across instruments and operators. In the present study, we developed an objective method to optimize the FIM segmentation threshold using poly(methyl methacrylate) (PMMA) beads with a refractive index similar to that of biomolecules. Among several candidate particles that were evaluated, 2.5-µm PMMA beads were the most reliable in size and number, suggesting that the PMMA bead size analyzed by FIM could objectively be used to determine the segmentation threshold for SVP measurements. The PMMA bead concentrations measured by FIM were highly consistent with the indicative concentrations, whereas the PMMA bead size analyzed by FIM decreased with increasing segmentation threshold. The optimal segmentation threshold where the analyzed size was closest to the indicative size differed between an instrument with a black-and-white camera and that with a color camera. Inter-instrument differences in SVP concentrations in acid-stressed recombinant adeno-associated virus (AAV) and protein aggregates were successfully minimized by setting an optimized segmentation threshold specific to the instrument. These results reveal that PMMA beads can aid in determining a more appropriate segmentation threshold to evaluate biopharmaceutical SVPs using FIM.

Peak Detection Algorithm for Mass Spectrometry Integrating Weighted Continuous Wavelet Transform with Particle Swarm Optimization-Based Otsu

Peak detection is an important step in mass spectrometry as accurately identifying characteristic peaks is key to data analysis. In order to address the issue of false peak detection, while simultaneously ensuring accurate detection of weak and overlapped peaks, this paper introduces an improved algorithm for mass spectrometry integrating weighted continuous wavelet transform with particle swarm optimization-based Otsu (WWTPO). The algorithm applies the weighted continuous wavelet transform (WCWT) to compress the frequency spectrum signal into a smaller scale range, which allows for the acquisition of more distinct and informative peak information. Moreover, the algorithm employs the particle swarm optimization (PSO) algorithm to iteratively evaluate the optimal image segmentation threshold, which addresses the challenge of inaccurate Otsu image segmentation. The method was applied to detect simulated peaks as well as matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) datasets. The performance evaluation was conducted using receiver operating characteristic (ROC) curves, F1 measure and F-scores. Through comparison with continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (WSTGA), multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS), the results demonstrate that WWTPO exhibits excellent performance in peak detection. The determination of 4-isopropyltoluene also demonstrates that WWTPO has excellent practical application. This method not only maintains a low false peak identification rate but also detects more weak peaks and overlapping peaks, further improving the accuracy and efficiency of peak detection in mass spectrometry.

UAV-Based Terrain Modeling in Low-Vegetation Areas: A Framework Based on Multiscale Elevation Variation Coefficients

The removal of low vegetation is still challenging in UAV photogrammetry. According to the different topographic features expressed by point-cloud data at different scales, a vegetation-filtering method based on multiscale elevation-variation coefficients is proposed for terrain modeling. First, virtual grids are constructed at different scales, and the average elevation values of the corresponding point clouds are obtained. Second, the amount of elevation change at any two scales in each virtual grid is calculated to obtain the difference in surface characteristics (degree of elevation change) at the corresponding two scales. Third, the elevation variation coefficient of the virtual grid that corresponds to the largest elevation variation degree is calculated, and threshold segmentation is performed based on the relation that the elevation variation coefficients of vegetated regions are much larger than those of terrain regions. Finally, the optimal calculation neighborhood radius of the elevation variation coefficients is analyzed, and the optimal segmentation threshold is discussed. The experimental results show that the multiscale coefficients of elevation variation method can accurately remove vegetation points and reserve ground points in low- and densely vegetated areas. The type I error, type II error, and total error in the study areas range from 1.93 to 9.20%, 5.83 to 5.84%, and 2.28 to 7.68%, respectively. The total error of the proposed method is 2.43–2.54% lower than that of the CSF, TIN, and PMF algorithms in the study areas. This study provides a foundation for the rapid establishment of high-precision DEMs based on UAV photogrammetry.

Comparing Water Indices for Landsat Data for Automated Surface Water Body Extraction under Complex Ground Background: A Case Study in Jilin Province

Derived from Landsat imagery and capable of enhancing the contrast between surface water bodies and the background, water indices are widely used in surface water body extraction. Whether one index and its optimal threshold can maintain the best all year round is a question. At present, however, little research has considered the effect of time or conducted experiments with data from different months. To identify the best index for surface water body extraction, two regions in Jilin Province were selected for the case study and a comprehensive comparative analysis considering the imagery acquisition time was conducted. Ten water indices were selected and calculated based on the 30 m spatial resolution Landsat TM/OLI imagery acquired in 1999 and 2001 and 2019 and 2021 from May to October. The indices included the Modified Normalized Difference Water Index (NDWI3 and MNDWI), Automated Water Extraction Index (AWEI) for images with and without shadow, Multi-Band Water Index (MBWI), New Water Index (NWI), Water Ratio Index (WRI), Sentinel-2 Water Index (SWI) originally calculated based on the Sentinel-2 imagery, New Comprehensive Water Index (NCIWI), Index of Water Surfaces (IWS), and Enhanced Water Index (EWI). The OTSU algorism was adopted to adaptively determine the optimal segmentation threshold for each index and the indices were compared in terms of inter-class separability, threshold sensitivity, optimal threshold robustness, and water extraction accuracy. The result showed that NWI and EWI performed the best in different months and years, with the best water enhancement effect that could suppress background information, especially for the water-related land use types and cloud pollution. Their optimal segmentation thresholds throughout the study period were more stable than others, with the ranges of −0.342 to −0.038 and −0.539 to −0.223, respectively. Based on the optimal thresholds, they achieved overall accuracies of 0.952 to 0.981 and 0.964 to 0.981, commission errors of 0 to 28.2% and 0 to 7.7%, and omission errors of 0 to 15% and 0 to 8%, with a kappa coefficient above 0.8 indicating good extraction results. The results demonstrated the effectiveness of NWI and EWI combined with the OTSU algorithm in better monitoring surface water during different water periods and offering reliable results. Even though this study only focuses on the lakes within two regions, the indices have the potential for accurately monitoring the surface water over other regions.

Quantitative characterization of pore-fracture structure of medium and high-rank coal based on micro-CT technology

ABSTRACT Coal is a natural and complex porous medium, which contains pore-fracture structures of different scales. The pore-fracture structures of coal directly affect the characteristics of gas storage and migration. In this paper, high-resolution X-ray CT scanning technology is used to quantitatively characterize the pore-fracture structure of medium and high-rank coals. First, the porosity inversion method is used to determine the optimal threshold segmentation of pores and fracture in medium and high-rank coals. Then, the pore and fracture model of representative element volume (REV) was extracted and established to characterize the area porosity, pore surface area, and shape factor of the two coals, and the relationship between pore surface area and shape factor was investigated. Finally, an equivalent pore network model (PNM) of connected pore topology was established, and pore-throat parameters are statistically analyzed, including pore volume, pore radius, throat radius, throat length, and coordination number. The results show that the average pore diameter and average surface area of macropores in medium-rank coals are larger than those of high-rank coals, and high-rank coals have fewer isolated pores. With the increase of coal rank, the length and radius of the throat gradually decrease, which indicates that the throat of the medium-rank coal has a higher degree of development, and the gas seepage ability in the throat is stronger. The outcome of this study is of great significance for comprehensively understanding the pore-fracture structure of coal in micro-scale.

Rayleigh-based segmentation of ISAR images.

Inverse synthetic aperture radar (ISAR) provides a solution to increase the radar angular resolution by observing a moving target over time. The high-resolution ISAR image should undergo a segmentation step to get the target's point cloud data, which is then used for classification purposes. Existing segmentation algorithms seek an optimal threshold in an iterative manner, which adds to the complexity of ISAR and results in an increase in the processing time. In this paper, we take advantage of the distribution of the ISAR image intensity, which is based on the Rayleigh distribution, and obtain an explicit relationship for the optimal segmentation threshold. The proposed segmentation algorithm alleviates the requirement for iterative optimization and its efficiency is shown using both simulated and experimental ISAR images.

High-performance compression-based brain tumor detection using lightweight optimal deep neural network

Medical image-processing plays a vital role in the brain tumor detection and diagnosis process for supporting the automation of computerized based analysis. Magnetic Resonance Image (MRI) is widely considered an imaging modality used for the diagnosis of brain tumors. The primary concern of this research is to detect and reduce the affected tumor region from MRI images and segmentation. But it is a tedious and more challenging task. Most brain tumors are diagnosed after symptoms appear. Brain tumor is a life-threatening problem and hampers the normal functioning of the human body. The noise interference is the major problem in image compression. To overcome these kinds of issues computerized technology is proposed. In this research, to reduce the complexity and improve the performance of detecting the brain tumor in the medical image segmentation process, an optimal threshold segmentation approach is introduced. In this proposed approach, a Lightweight optimal Deep Learning Technique and an effectual compression standard technique are proposed to improve the quality of an image and accuracy level. Using this proposed approach accurate classification is done by minimizing the error. This paper combines the Whale optimization algorithm (WOA) and the Black Widow Optimization algorithm (BWO) to detect the tumor. This hybrid technique is used to detect the severity of the brain tumor. The analytical results of the suggested method have been calculated and verified for quality and performance analysis on MRI images, relying on dice coefficient, specificity, accuracy, structural similarity index, and sensitivity. This research showed that our approach is better than the previous research according to the quality standards and accuracy. The accuracy of our proposed work is greater than the other techniques.

The Effect of Segmentation Threshold on Computational Fluid Dynamic Analysis of Nasal Airflow.

The objective analysis of nasal airflow stands to benefit greatly from the adoption of computational fluid dynamic (CFD) methodologies. In this emerging field, no standards currently exist in regard to the ideal modeling parameters of the nasal airway. Such standards will be necessary for this tool to become clinically relevant. Human nasal airways were modeled from a healthy control, segmented, and analyzed with an in-house immersed boundary method. The segmentation Hounsfield unit (HU) threshold was varied to measure its effect in relation to airflow velocity magnitude and pressure change. Surface area and volume have a linear relationship to HU threshold, whereas CFD variables had a more complex relationship. The HU threshold should be included in nasal airflow CFD analysis. Future work is required to determine the optimal segmentation threshold.

Combination of continuous wavelet transform and genetic algorithm-based Otsu for efficient mass spectrometry peak detection

Mass spectrometry (MS) data is susceptible to random noises and alternating baseline, posing great challenges to spectral peak detection, especially for weak peaks and overlapping peaks. Herein, an efficient peak detection algorithm combining continuous wavelet transform (CWT) and genetic algorithm-based threshold segmentation (denoted as WSTGA) for mass spectrometry was proposed. Firstly, Mexican Hat wavelet was selected as the mother wavelet by comparing the matching degree between the difference of Gaussian (DOG) and different wavelets. Subsequently, the ridges and valleys were identified from 2D wavelet coefficient matrix. Afterward, an improved threshold segmentation method, Otsu method based on genetic algorithm, was introduced to find optimal segmentation threshold and achieve better image segmentation, overcoming the deficiency of traditional Otsu method that cannot handle long-tailed unimodal histograms. Finally, the characteristic peaks were successfully identified by utilizing the ridge-valley lines in wavelet space and original spectrum. Receiver operating characteristic (ROC) curve, area under curve (AUC) and F₁ measure are used as criterions to evaluate performance of peak detection algorithms. Compared with multi-scale peak detection (MSPD) and CWT and image segmentation (CWT-IS) methods, all the results showed that WSTGA can achieve better peak detection. More importantly, the experimental results from MALDI-TOF spectra demonstrated that WSTGA can effectively detect more weak peaks and overlapping peaks while maintaining a lower false peak detection rate than MSPD and CWT-IS methods, indicating its great advantages in characteristic peak identification.

Research on Intelligent Recognition and Management of Smart City Based on Machine Vision

In recent years, with the rapid development of science and technology, people’s demand for the quality of daily production and life has gradually increased, and all localities are gradually urbanized. The convenient use of water conservancy, transportation, environment, medical treatment, power grid and other aspects of cities in any country affects people’s healthy life. Therefore, it is urgent to build a smart city. For smart city, intelligent identification and management is a very large and complex problem. As one of the high and new technologies, the intelligent recognition of machine vision derived from artificial intelligence has always been a hot spot of great concern. It just provides convenience for the development of smart city and becomes the development direction of smart city construction in the future. Based on this, the role of machine vision is to connect with the computer through wireless sensors such as cameras to simulate an eye that can represent human visual function. This simulated eye can be recognized intelligently in real time. It transmits the recognized information to the computer, and the computer will analyze and process the obtained information for judgment and recognition. This paper will mainly use the optimal threshold segmentation algorithm based on Machine Vision video processing to solve the problem of urban intelligent recognition, and use a specific algorithm to solve some difficulties and obstacles in intelligent recognition. The traditional optimal threshold segmentation algorithm, the optimal threshold segmentation algorithm and the improved optimal threshold segmentation algorithm are experimentally compared. After experimental comparison, it is found that the optimal threshold segmentation algorithm, the improved optimal threshold segmentation algorithm and the traditional optimal threshold segmentation algorithm can intelligently identify the types of buildings and urban traffic signs in the city, Compared with the improved optimal threshold segmentation algorithm, the improved optimal threshold segmentation algorithm improves the response speed, real-time performance, stability and accuracy of the algorithm. Therefore, the optimal threshold segmentation algorithm after the well meets the needs of building the system, and can be useful in the process of intelligent recognition. This improvement is also necessary, which is conducive to the subsequent system construction.

Enhanced Fuzzy Elephant Herding Optimization-Based OTSU Segmentation and Deep Learning for Alzheimer’s Disease Diagnosis

Several neurological illnesses and diseased sites have been studied, along with the anatomical framework of the brain, using structural MRI (sMRI). It is critical to diagnose Alzheimer’s disease (AD) patients in a timely manner to implement preventative treatments. The segmentation of brain anatomy and categorization of AD have received increased attention since they can deliver good findings spanning a vast range of information. The first research gap considered in this work is the real-time efficiency of OTSU segmentation, which is not high, despite its simplicity and good accuracy. A second issue is that feature extraction could be automated by implementing deep learning techniques. To improve picture segmentation’s real-timeliness, enhanced fuzzy elephant herding optimization (EFEHO) was used for OTSU segmentation, and named EFEHO-OTSU. The main contribution of this work is twofold. One is utilizing EFEHO in the recommended technique to seek the optimal segmentation threshold for the OTSU method. Second, dual attention multi-instance deep learning network (DA-MIDL) is recommended for the timely diagnosis of AD and its prodromal phase, mild cognitive impairment (MCI). Tests show that this technique converges faster and takes less time than the classic OTSU approach without reducing segmentation performance. This study develops a valuable tool for quick picture segmentation with good real-time efficiency. Compared to numerous conventional techniques, the suggested study attains improved categorization performance regarding accuracy and transferability.

Machine vision based insulator image chip drop fault recognition method

Abstract The paper studies the method of insulator image chip drop fault recognition based on machine vision to improve the recognition effect of insulator chip drop fault.Zhang’s calibration method was used to calibrate the internal parameters of binocular vision camera to improve the results in image acquisition. Insulator images were captured by binocular vision camera after calibration. The color image is converted to digital image and the insulator target image is extracted from the saturation component of a digital image by the optimal entropy threshold segmentation method. The nonlinear function is used to correct the high-frequency subband coefficients in different scales and different directions in the insulator target image, and the coefficients are used to extract the high-frequency information of different scales and directions.. The improved one-dimensional Hough transform detection algorithm is used to identify the ellipse in the edge image, that is, the insulator. The decision conditions of fault identification are designed to complete the fault identification of insulator drop. Experiments show that this method can clearly collect insulator images under different weather conditions, effectively segment insulator target images and accurately extract insulator edge information. The method can accurately identify the insulator drop fault based on the ellipse detection results. When the contrast and brightness factors are different, the f1-score value of fault recognition is higher and the fault recognition effect is better.

Boosting Marine Predators Algorithm by Salp Swarm Algorithm for Multilevel Thresholding Image Segmentation.

Pixel rating is considered one of the commonly used critical factors in digital image processing that depends on intensity. It is used to determine the optimal image segmentation threshold. In recent years, the optimum threshold has been selected with great interest due to its many applications. Several methods have been used to find the optimum threshold, including the Otsu and Kapur methods. These methods are appropriate and easy to implement to define a single or bi-level threshold. However, when they are extended to multiple levels, they will cause some problems, such as long time-consuming, the high computational cost, and the needed improvement in their accuracy. To avoid these problems and determine the optimal multilevel image segmentation threshold, we proposed a hybrid Marine Predators Algorithm (MPA) with Salp Swarm Algorithm (SSA) to determine the optimal multilevel threshold image segmentation MPASSA. The obtained solutions of the proposed method are represented using the image histogram. Several standard evaluation measures, such as (the fitness function, time consumer, Peak Signal-to-Noise Ratio, Structural Similarity Index, etc.…) are employed to evaluate the proposed segmentation method’s effectiveness. Several benchmark images are used to validate the proposed algorithm’s performance (MPASSA). The results showed that the proposed MPASSA got better results than other well-known optimization algorithms published in the literature.

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu.

Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.