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Serious ice accretion will cause structural problems and ice flashover accidents, which result in outdoor insulator string operating problems in winter conditions. Previous investigations have revealed that the thicker and longer insulators are covered with ice, the icing degree becomes worse and icing accident probability increases. Therefore, an image processing method was proposed to extract the characteristics of the icicle length and Rg (ratio of the air gap length to the insulator length) of ice-covered insulators for monitoring the operation of iced outdoor insulator strings. The tests were conducted at the artificial climate room of CIGELE Laboratories recommended by IEEE Standard 1783/2009. The surface phenomena of the insulator during the ice accretion process were recorded by using a high-speed video camera. In the view of the ice in the background of the picture of fuzzy features and high image noise, a direct equalization algorithm is used to enhance the grayscale iced image contrast. The median filtering method is conducted for reducing image noise and sharpening the image edge. The maximum entropy threshold segmentation algorithm is put forward to extract the insulators and its surface ice from the background. Then, the modified Canny operator edge detection algorithm is selected to trace the boundaries of objects through the extraction of information about attributes of the endpoints of edges. After we obtained the improved Canny edge detection image for both of the ice-covered insulators and non-iced insulators, the icing thickness can be obtained by calculating the difference between the edge of the non-iced insulators image and the edge of the iced insulator image. Besides, in order to identify the icing degree of the insulators more accurately, this paper determines the location of icicles by using the region growth method. After that, the icicle length and Rg can be obtained to monitor the icing degree of the insulator. It will be helpful to improve the ability to judge the accident risk of insulators in power systems.

With the rapid development of computer and artificial intelligence technology, infrared images are playing a higher and higher value in the state monitoring of electrical equipment in substations. Aiming at the detection of the oil level area of the oil conservator in the substation, in order to accurately obtain the oil level information, this paper proposes a transformer oil level detection method combining maximum entropy threshold segmentation algorithm and SLIC (simple linear iterativeclustering) segmentation algorithm. First, the original infrared image is pre-processed by image cutting technology to obtain the infrared image of the oil pillow area; the green channel image is extracted, and then the infrared image is segmented by the maximum entropy threshold segmentation algorithm to remove the background interference and obtain the target area of the electrical equipment. Finally, SLIC is used to super the pixel segmentation algorithm performs segmentation to obtain the oil level area. Experiments show that the algorithm can clearly segment the oil level area of the oil pillow, and it has certain practical value.

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder, and its early diagnosis critically depends on accurate segmentation of brain pathological images. However, conventional multi-threshold image segmentation (MIS) methods often exhibit high sensitivity to noise and insufficient exploitation of spatial structural information, particularly when applied to AD images with complex textures and dense information content. To overcome these limitations, this study introduces a novel three-dimensional (3D) Rényi entropy model that integrates grayscale intensity, non-local means (NLM), and local entropy. The resulting joint histogram simultaneously captures grayscale, spatial, and texture features, enabling a more comprehensive characterization of image uncertainty. To effectively optimize the high-dimensional threshold space, we propose a Quantum Hybrid Electric Eel Foraging Optimization (QHEEFO) algorithm. QHEEFO incorporates a quantum tunneling strategy (QTS), quantum control factors, and a logarithmic-enhanced perturbation mechanism to enhance jump capability and global search dynamics. Importantly, it mitigates the center bias effect inherent in the original EEFO algorithm, which tends to cause premature convergence around centroidal solutions. Additionally, QHEEFO integrates three synergistic strategies: a Gauss/mouse chaotic map (GCM) to diversify the initial population, a population self-learning (PSL) mechanism to promote cooperative evolution, and an elite pooling mutation (EPM) strategy to improve local refinement stability and precision. Extensive experiments on the IEEE CEC 2017 benchmark functions demonstrate that QHEEFO achieves comparable or superior optimization accuracy while reducing function evaluations by approximately 60%. Further validation on breast cancer and self-constructed AD image datasets confirms its superior segmentation performance and robustness, underscoring its potential in real-world medical image analysis applications.

Medical image segmentation is a critical and demanding step in medical image processing, which provides a solid foundation for subsequent medical image data extraction and analysis. Multi-threshold image segmentation, one of the most commonly used and specialized image segmentation techniques, limits its application to medical images because it requires demanding computational performance and is difficult to produce satisfactory segmentation results. To overcome the above problems, an ameliorated Fick's law algorithm (MsFLA) for multi-threshold image segmentation is developed in this paper. First, an optimized sine–cosine strategy is introduced to extend the molecular diffusion process to alleviate the problem of easily falling into local optima, thus improving the convergence accuracy of the Fick's law algorithm (FLA). Secondly, the introduction of local minimal value avoidance enriches the individual molecular information and enhances the local search ability, thus improving computational accuracy. In addition, the optimal neighborhood learning strategy is added to ensure a more careful and reasonable reliance on the optimal solution, thus reducing the chance of convergence of a local solution. The efficient optimization capability of MsFLA is comprehensively validated by comparing MsFLA with the original FLA and other algorithms in 23 classical benchmark functions. Finally, MsFLA is applied to image segmentation of grayscale images of COVID-19 and brain and color images of Lung and Colon cancer histopathology by using Cross entropy to validate its segmentation capability. The experimental results show that the MsFLA obtains the best segmentation results in three medical image cases compared to other comparison algorithms, which indicates that MsFLA can effectively solve the multi-threshold medical image segmentation problem. Graphical abstract

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search methods are costly in determining optimal thresholds and the conventional remora optimization algorithm (ROA) is prone to the premature convergence. This paper presents a chimp-inspired remora optimization algorithm (HCROA) to search optimal threshold levels, and the cross-entropy is employed as the objective function. In HCROA, the particles’ position are adjusted by the Chimp Optimization Algorithm (ChOA) because of its good exploitation ability and sufficient diversity. With this change, HCROA achieves both the intra-group diversity intelligence and a suitable balance between exploration and exploitation. To validate its performance, a series of experiments are performed. First, we test the HCROA’s segmentation accuracy by a set of natural gray-scale images with different thresholds. Second, HCROA is implemented for noisy image segmentation to evaluate its robustness. Several reference-based measurements including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Feature Similarity (FSIM), Quality Index based on Local Variance (QILV), Haar wavelet-based Perceptual Similarity Index (HPSI), Wilcoxon test, and CPU time have been considered for evaluating the proposed method. Additionally, eight well-known predecessors are injected for parallel comparison. The comparison results prove that the suggested method outperforms the existing approaches in terms of accuracy, convergence speed, noise robustness, and efficiency.

Threshold segmentation based on swarm intelligence optimization algorithm is a research hotspot in image processing, because of its good segmentation effect and easy implementation. This paper proposes an image threshold segmentation method based on an improved sparrow search algorithm and 2-D maximum entropy method. In the proposed algorithm, the nonlinear inertia weight is introduced into the entrants’ update formula to improve the local exploration ability of the algorithm, and Levy flight is introduced into the vigilant sparrows’ update formula to prevent the algorithm from falling into the local optimal solution in the later stage of iteration. In addition, improved sparrow search algorithm is tested on fifteen benchmark functions. The results represent the merit of the proposed algorithm with respect to other algorithms. Finally, the proposed algorithm is applied to entropy based image segmentation. Experiment results on classical images and medical images show that the proposed method improves the segmentation effect in terms of peak signal-to-noise ratio and feature similarity.

This paper proposes a multi-threshold image segmentation method based on modified salp swarm algorithm (SSA). Multi-threshold image segmentation method has good segmentation effect, but the segmentation precision will be affected with the increase of threshold number. To avoid the above problem, the slap swarm optimization algorithm (SSA) is presented to choose the optimal parameters of the fitting function and we use levy flight to improve the SSA. The solutions are assessed using the Kapur's entropy, Otsu and Renyi entropy fitness function during the optimization operation. The performance of the proposed algorithm is evaluated with several reference images and compared with different group algorithms. The results have been analyzed based on the best fitness values, peak signal to noise ratio (PSNR), and feature similarity index measures (FSIM). The experimental results show that the proposed algorithm outperformed other swarm algorithms.

Image segmentation is an initial task in many vision-based systems and plays an important role in the processes of image analysis, target recognition and tracking, and image thresholding techniques have been widely used because of their simplicity, efficiency and robustness.Tsallis entropy thresholding method is an information-theoretic based thresholding criterion, a global threshold selection method extended from Shannon entropy, which it is assumed that the target and background are independent of each other, and the Tsallis entropy thresholding method may fail if the assumption is not satisfied. To address this, this paper proposes the Tsallis entropy correlation image thresholding method using the Tsallis entropy correlation concept defined by Tsallis, which has the property of generalized entropy and can be applied to the case where the target and the background are not independent of each other. To address the problem that the optimal segmentation threshold computation increases exponentially with the number of thresholds faced by the multi-threshold case, a recursive dynamic programming algorithm under the Tsallis entropy correlation criterion is proposed in the paper. To illustrate the effectiveness of the proposed method, we perform experimental simulations on 20 images from different datasets and compare the image segmentation quality under the original Tsallis entropy, error Tsallis entropy and Tsallis entropy correlation, and the results show that the Tsallis entropy correlation criterion has a good segmentation effect. We also compare the exhaustive method, recursive method, dynamic programming, recursive-based dynamic programming algorithm and particle swarm optimization algorithm to solve the optimal solution of the Tsallis entropy correlation criterion under multi-thresholds, respectively, and the experiments show that our proposed recursive-based dynamic programming algorithm has better stability and lower time complexity, and can effectively solve the optimal solution under multi-thresholds.

Segmentation of medical images is a crucial step in medical diagnosis, essential for accurate disease detection and treatment planning. However, traditional multi-threshold image segmentation techniques often face challenges such as high computational demands and susceptibility to local optima. This study aims to address these challenges by introducing an enhanced optimization algorithm, GBJAYA, which integrates the Gaussian bare-bone strategy to improve segmentation performance. The proposed GBJAYA algorithm incorporates Gaussian-distributed random number update mechanisms to enhance global search capabilities and accelerate convergence. The algorithm’s effectiveness was evaluated through experiments on IEEE CEC2017 benchmark functions and two types of medical images. Performance was assessed using metrics such as PSNR, SSIM, and FSIM, and statistical validation was conducted using Friedman and Wilcoxon tests. The results demonstrate that GBJAYA outperforms 10 basic and 10 improved algorithms, achieving lower mean values and smaller standard deviations in most tests. The algorithm exhibited superior segmentation performance and stability, as confirmed by convergence curve analysis, which also highlighted its rapid convergence and ability to avoid local optima. The GBJAYA significantly enhances medical image segmentation, offering superior performance, stability, and fast convergence. These findings demonstrate its broad potential for application in medical diagnosis and treatment planning.

Melanoma is a malignant tumor formed by the cancerous transformation of melanocytes, and its medical images contain a lot of information. However, the percentage of the key information in the image is small and the noise is non-uniformly distributed. We propose a new multi-threshold image segmentation model based on the two-dimensional (2D) histogram approach to the above problem. In the proposed model, we present an enhanced ant colony optimization for continuous domains (EACOR) based on the soft besiege strategy and the chase strategy. Further, EACOR is combined with 2D Kapur's entropy to search for the optimal thresholds. An experiment on the IEEE CEC2014 benchmark function was conducted to measure the reliable global search capability of the EACOR algorithm in the proposed model. Moreover, we have also carried out several sets of experiments to test the validity of the image segmentation model proposed in this paper. The experimental results show that the segmented images obtained from the proposed model outperform the comparison method in several evaluation metrics. Ultimately, the model proposed in this paper can provide high-quality samples for subsequent analysis of melanoma pathology images.