Search

To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm's search diversity, local pioneering ability, and convergence accuracy. A levy flight strategy further improves the algorithm's ability to jump out of local minima. In this paper, 12 benchmark test functions and 8 other newer swarm intelligence algorithms are compared in terms of convergence speed and convergence accuracy to evaluate the performance of the MSIPOA algorithm. By non-parametric statistical analysis, MSIPOA shows a greater superiority over other optimization algorithms. The MSIPOA algorithm is then experimented with symmetric cross-entropy multilevel threshold image segmentation, and eight images from BSDS300 are selected as the test set to evaluate MSIPOA. According to different performance metrics and Fridman test, MSIPOA algorithm outperforms similar algorithms in global optimization and image segmentation, and the symmetric cross entropy of MSIPOA algorithm for multilevel thresholding image segmentation method can be effectively applied to multilevel thresholding image segmentation tasks. [ABSTRACT FROM AUTHOR]

Multilevel thresholding is a basic method in image segmentation. The conventional image multilevel thresholding algorithms are computationally expensive when the number of decomposed segments is high. In this paper, a novel and powerful technique is suggested for Crow Search Algorithm (CSA) devoted to segmentation applications. The main contribution of our work is to adapt Darwinian evolutionary theory with heuristic CSA. First, the population is divided into specified groups and each group tries to find better location in the search space. A policy of encouragement and punishment is set on searching agents to avoid being trapped in the local optimum and premature solutions. Moreover, to increase the convergence rate of the proposed method, a gray-scale map is applied to out-boundary agents. Ten test images are selected to measure the ability of our algorithm, compared with the famous procedure, energy curve method. Two popular entropies i.e. Otsu and Kapur are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are implemented and compared to the introduced method. The obtained results show that our method, compared with the original CSA, and other heuristic search methods, can extract multi-level thresholding more efficiently. [ABSTRACT FROM AUTHOR]

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on Cuckoo search (CS) is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to a fall into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with CS algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of CS algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Two popular entropies criteria, Otsu and Kapur, are employed to evaluate the capability of the introduced algorithm. Eight different search algorithms are also implemented and compared with our method. Experimental results manifest that DCS is a powerful tool for multilevel thresholding and the obtained results outperform the CS algorithm and other heuristic search methods. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation has always been a popular issue and has attracted much attention. Traditional exhaustive search methods take considerable time to solve multilevel thresholding problems. However, heuristic search algorithms have potential advantages in terms of solving such multilevel thresholding problems. Based on this idea, in this paper, a novel adaptive gravitational search algorithm (AGSA) is proposed to solve the optimal multilevel image thresholding problem; this algorithm is more efficient than the traditional exhaustive search method for grayscale image segmentation. In the AGSA, an adaptive parameter optimization strategy is used to tune the gravitational constant and the inertia weight. To verify the performance of the proposed algorithm, a series of classic test images are used to perform several experiments. In addition, the standard GSA and some optimization algorithms are compared with the proposed algorithm. The experimental results show that the proposed algorithm is obviously better than the other six algorithms. These promising results suggest that the AGSA is more suitable than existing methods for multilevel image thresholding. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low efficiency of rubber mark detection due to the diversity of morphological features of airport runway rubber mark and the complexity of the runway environment, this paper proposed a new airport runway rubber mark image segmentation algorithm based on simplified pulse coupled neural network. Firstly, on the basis of optimizing feedback input by using neighborhood gray-scale relationship, this algorithm improved the neuron ignition threshold mechanism from the traditional exponential decay to linear stratified step decay. Then, it combined the cuckoo search algorithm with the minimum cross entropy to optimize the parameters of the improved model. Finally, according to the average gray value of the ignition map ping area, it constructed an adaptive iterative termination condition. The experimental results show that the proposed algorithm has higher rubber mark detection accuracy and better anti-interference performance under the influence of poor lighting conditions and marking lines. [ABSTRACT FROM AUTHOR]

In view of the problems that the dragonfly algorithm has, such as that it easily falls into the local optimal solution and the optimization accuracy is low, an improved Dragonfly Algorithm (IDA) is proposed and applied to Otsu multi-threshold image segmentation. Firstly, an elite-opposition-based learning optimization is utilized to enhance the diversity of the initial population of dragonflies, laying the foundation for subsequent algorithm iterations. Secondly, an enhanced sine cosine strategy is introduced to prevent the algorithm from falling into local optima, thereby improving its ability to escape from local optima. Then, an adaptive t-distribution strategy is incorporated to enhance the balance between global exploration and local search, thereby improving the convergence speed of the algorithm. To evaluate the performance of this algorithm, we use eight international benchmark functions to test the performance of the IDA algorithm and compare it with the sparrow search algorithm (SSA), sine cosine algorithm (SCA) and dragonfly algorithm (DA). The experiments show that the algorithm performs better in terms of convergence speed and accuracy. At the same time, the Otsu method is employed to determine the optimal threshold, a series of experiments are carried out on six images provided by Berkeley University, and the results are compared with the other three algorithms. From the experimental results, the peak signal-to-noise ratio index (PSNR) and structural similarity index (SSIM) based on the IDA algorithm method are better than other optimization algorithms. The experimental results indicate that the application of Otsu multi-threshold segmentation based on the IDA algorithm is potential and meaningful. [ABSTRACT FROM AUTHOR]

Multilevel thresholding image segmentation is an important technique, which has attracted much attention in recent years. The conventional exhaustive search method for image segmentation is efficient for bilevel thresholding. However, they are time expensive when dealing with multilevel thresholding image segmentation. To better tackle this problem, an improved cuckoo search algorithm (ICS) is proposed to search for the optimal multilevel thresholding in this paper, and Otsu is considered as its objective function. In the ICS, two modifications are used to improve the standard cuckoo search algorithm. First, a parameter adaptation strategy is utilized to improve exploration performance. Second, a dynamic weighted random-walk method is adopted to enhance the local search efficiency. A total of six benchmark test images are used to perform the experiments, and seven state-of-the-art metaheuristic algorithms are introduced to compare with the ICS. A series of measure indexes such as objective function value and standard deviation, PSNR, FSIM, and SSIM as well as the Wilcoxon rank sum and convergence performance are performed in the experiments; the experimental results show that the proposed algorithm is superior to other seven well-known heuristic algorithms. [ABSTRACT FROM AUTHOR]

• An improved Crow Search Algorithm (VMCSA) is proposed. • Two effective position updating strategies are introduced into CSA. • The performance of VMCSA is verified by comparison with some excellent peers. • An effective image segmentation method based on VMCSA is proposed. • VMCSA is tested on standard benchmark and COVID-19 image segmentation problems. COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. This work introduces a new image segmentation method based on the constriction coefficient‐based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC‐SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate. Various performance metrics are employed to investigate the simulation outcomes, including optimal thresholds, standard deviation, MSE (mean square error), run time analysis, PSNR (peak signal to noise ratio), best fitness value calculation, convergence maps, segmented image graphs, and box plot analysis. Moreover, image accuracy is benchmarked by utilizing SSIM (structural similarity index measure) and FSIM (feature similarity index measure) metrics. Also, a pairwise non‐parametric signed Wilcoxon rank‐sum test is utilized for statistical verification of simulation results. In addition, the experimental outcomes of CPSOGSA are compared with eight different algorithms including standard PSO, classical GSA, PSOGSA, SCA (sine cosine algorithm), SSA (salp swarm algorithm), GWO (grey wolf optimizer), MFO (moth flame optimizer), and ABC (artificial bee colony). The simulation results clearly indicate that the hybrid CPSOGSA has successfully provided the best SSIM, FSIM, and threshold values to the benchmark images. [ABSTRACT FROM AUTHOR]

Gray-scale image segmentation can be cast as a combinatorial optimization problem using a Markov Random Field (MRF) model. Local and global search algorithms were proposed to find an optimal solution in a reasonable amount of time. Both local and global search algorithms have their strengths and their shortcomings. The aim of this work is to propose and study a combination between local and global search strategies. For the local search we use the Iterated Conditional Modes (ICM) while the global optimization is performed by an Ant Colony System (ACS). ICM and ACS are both based on a MRF model to assess the local and the global quality of an arbitrary segmentation. The local optimization is performed by combining Maximum Likelihood (ML) for estimation of the parameters and ICM to find the solution given a fixed set of these parameters. A comparative study is performed to select the combination strategy that gives a better result. The effectiveness of our proposal is proven through experimentation. The problem of parameterization of our solution is addressed through an empirical study that shows the effect of each parameter on the behavior of the proposed solution. [ABSTRACT FROM AUTHOR]

Deep learning (DL) algorithms have become an increasingly popular choice for image classification and segmentation tasks; however, their range of applications can be limited. Their limitation stems from them requiring ample data to achieve high performance and adequate generalizability. In the case of clinical imaging data, images are not always available in large quantities. This issue can be alleviated by using data augmentation (DA) techniques. The choice of DA is important because poor selection can possibly hinder the performance of a DL algorithm. We propose a DA policy search algorithm that offers an extended set of transformations that accommodate the variations in biomedical imaging datasets. The algorithm makes use of the efficient and high-dimensional optimizer Bi-Population Covariance Matrix Adaptation Evolution Strategy (BIPOP-CMA-ES) and returns an optimal DA policy based on any input imaging dataset and a DL algorithm. Our proposed algorithm, Medical Augmentation (Med-Aug), can be implemented by other researchers in related medical DL applications to improve their model's performance. Furthermore, we present our found optimal DA policies for a variety of medical datasets and popular segmentation networks for other researchers to use in related tasks. [ABSTRACT FROM AUTHOR]