Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy

Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation

Image segmentation is a platform for many high-level applications. It aims to identify the different homogeneous regions of an image. In our work, we cast the problem as a parallel distributed optimization task and we present a new method for image segmentation by multilevel thresholding based on generalized island model (GIM). This model is characterized by the cooperation of three metaheuristics namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Artificial Bee Colony (ABC). The cooperation is ensured by the use of a migration pattern the purpose of which is to avoid getting stuck in local optima. The effectiveness of the proposed method has been assessed and compared to each of the algorithms of the GIM model when used separately and other methods from the literature like Darwinian PSO (DPSO), Fractional-Order Darwinian PSO (FO-DPSO). The obtained results are very promising and show the positive impact of the use of a parallel distributed model. They also show that the proposed method competes and even outperforms the other methods.

There are many techniques of image enhancement as well as optimizing the same process in the image processing such as Powell’s method, Fast Ostu’s method, Histogram equalization (HE) method, Particle swarm optimization (PSO) and Honey bee (HB) algorithm. In this paper, a new approach to automatic image enhancement using honey bee mating optimization is implemented by specifying intensity of the edges pixels and also earlier reported PSO results were used. Further results of previous work i-e PSO and HBMO are compared through different parameters. The obtained results indicate that the proposed Honey bee algorithm yields better results in the terms of both the maximization of number of the pixels in the edges and peak signal to noise ratio (PSNR). Computational time is also relatively small in the Honey bee enhanced image as compared to the PSO case which is earlier reported. Keyword: Swarm optimization, honey bee mating optimization and image enhancement.

Today using evolutionary programing for solving complex, nonlinear mathematical problems like optimum power flow is commonly in use. These types of problems are naturally nonlinear and the conventional mathematical methods aren’t powerful enough for achieving the desirable results. In this study an Optimum Power Flow problem solved by means of minimization of fuel costs for IEEE 30 buses test system by Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Honey Bee Mating Optimization (HBMO) and Shuffle Frog Leaping Algorithm (SFLA), these algorithms has been used in MATLAB medium with help of MATHPOWER to achieving more precise results and comparing these results with the other proposed results in other published papers.

The rapid advance of artificial intelligence has made complex image processing in real time possible. Multilevel thresholding has become a feasible way for image segmentation, even in the presence of poor contrast and external artefacts. Genetic algorithms (GAs) and particle swarm optimisation (PSO) are broadly recognised by far to be two dominating schemes which outperform classical ones on multilevel thresholding. Qualitative analysis can usually be applied to observe their superiority to all classical approaches. However, no convincing result is reached with respect to differences in performance between GAs and PSO. The existing segmentation practices are either examined by visual appeals exclusively, or evaluated quantitatively assuming perfect statistical distributions. To make thorough comparisons, comparative analysis of two leading multilevel thresholding approaches is conducted for true colour image segmentation. The information theory is also employed to analyse the outcomes of systematic approaches using diverse quantitative metrics from various aspects.

BackgroundImage segmentation is an important step during the processing of medical images. For example, for the computer aid diagnostic systems for lung cancer image analysis, the segmented regions of tumors would help doctors in early diagnosis to determine timely and appropriate treatment possibilities and thereby improve the survival rate of the patients. However, general clinical routines of manual segmentation for large number of medical images are very difficult and time consuming, which is the challenge we aim to tackle using our proposed method.MethodsA novel image segmentation method with evolutionary learning technique named Group Theoretic Particle Swarm Optimization is proposed. It can tackle multi-level thresholding optimization problem during the segmentation process and rebuild the search paradigm according to the solid mathematical foundation of symmetric group from four designable aspects, which are particle encoding, solution landscape, neighborhood movement and swarm topology, respectively. The Kapur’s entropy of multi-level thresholds is assessed as the objective function.ResultsIn contrast to those conventional metaheuristics methods for lung cancer image segmentation, this newly presented method generates the best performance result among them. Experimental results show that its Kapur’s entropy has the value of 9.07, which is 16% higher than the worst case. Computational time is acceptable at the cost of 173.730 seconds, average level of evaluation metrics [Kappa, Precision, Recall, F1-measure, intersection over union (IoU) and receiver operating characteristic (ROC)] is over 90%, and search process of multi-level threshold combination would finally converge in the later phase of iterations after 700. The ablation study indicates that all components are significant to the contributions of our proposed method.ConclusionsGroup Theoretic Particle Swarm Optimization for multi-level threshold segmentation is an efficient way to split a medical image into distinct regions and extract tumor tissues regions from the background. It maintains the balanced relationship between diversification and intensification during the search process and helps clinicians to make the diagnosis more accurately. Our proposed method processes potential medical value and clinical meanings.

Evolutionary algorithms are used in many engineering applications for optimization of problems that are often difficult to solve using conventional methods. One such problem is image segmentation. This task is used for object (contour) extraction from images to create sensible representation of the image. There are many image segmentation and optimization methods. This work is focused on selected evolutionary optimization methods. Namely, particle swarm optimization, genetic algorithm, and differential evolution. Our image segmentation method is inspired in algorithm known as k-means. The optimization function from k-means algorithm is replaced by evolutionary technique. We compare original k-means algorithm with evolutionary approaches and we show that our evolutionary approaches easily outperform the classical approach.

Medical image segmentation using exchange market algorithm

Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.

Multilevel thresholding for image segmentation is a crucial process in several applications such as feature extraction and pattern recognition. The meticulous search for the best values for the optimization of fitness function using classical operations needs profuse computational time, which also results in inaccuracy and instability. In this paper, a new beta differential evolution (BDE)-based fast color image multilevel thresholding scheme using two objective functions has been presented. The optimal threshold values are determined by maximizing Kapur’s and Tsallis entropy (entropy criterion) thresholding functions coupled with BDE algorithm. The efficiency of the proposed method is examined over existing multilevel thresholding methods such as artificial bee colony, particle swarm optimization, wind-driven optimization and differential evolution. These approaches are aimed to determine optimum threshold values at different levels of thresholding for color image segmentation. The proficiency of the presented methodology is demonstrated visually and computationally on five real-life true color images as well as four satellite images. Experimental outcomes are exhibited in terms of the optimal threshold value, best objective function and computational cost (in seconds) for each method at different thresholding levels. Afterward, the proposed scheme is examined intensively regarding the superiority of quality. The experimentally evaluated results show that the proposed BDE-based approach for multilevel color image segmentation can accurately and efficiently examine for multiple thresholds, which are near to optimal ones searched using an exhaustive search process.

A fast image segmentation method with multilevel threshold of two-dimensional entropy was proposed based on the firefly algorithm to overcome the large amount of calculation and long computing time.Firstly,the principle of two-dimensional entropy threshold segmentation was analyzed,and the single threshold segmentation of two-dimensional entropy was extended to multilevel threshold segmentation.Then,the bionic mechanism and searching optimization process of the firefly algorithm were analyzed,and the multilevel threshold segmentation method of two-dimensional entropy combined with firefly algorithm was proposed.Finally,typical image segmentation experiments by using the proposed method were performed and the results were compared with those of two-dimensional entropy exhaustive segmentation method and the multilevel threshold segmentation method of two-dimensional entropy based on Particle Swarm Optimization(PSO).Experimental results show that the speeds of the proposed method in single threshold segmentation,dual-threshold segmentation and thethree threshold segmentation are 3.91,1 040.32and 8 128.85times faster than those of the two-dimensional entropy exhaustive segmentation method respectively.Moreover,the threshold selection accuracy and running speed of the proposed method are both better than those of the multilevel threshold segmentation method of two-dimensional entropy based on PSO.Therefore,the fast image segmentation method with multilevel threshold of two-dimensional entropy based on firefly algorithm can quickly and efficiently resolve complex and multi-target image segmentation problems.

New quantum inspired meta-heuristic techniques for multi-level colour image thresholding

The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

Image Segmentation based on multilevel thresholding using non-extensive (non-additive) entropy based techniques is challenging, and the optimal choice of thresholds is an effective approach to solve this problem. In this paper, we propose a novel optimization technique based on the Particle Swarm Optimization (PSO) called Fibonacci Particle Swarm Optimization (FPSO) that helps decide the optimal thresholds by maximizing the objective function of Tsallis entropy. The superiority of our proposed method has been demonstrated by comparing the results with some of the contemporary algorithms like Genetic Algorithm (GA), Bacterial Foraging Optimization (BFO), the Standard Particle Swarm Optimization (PSO) and the Golden Ratio Particle Swarm Optimization (GRPSO). The quality of the segmented images has been evaluated using Peak Signal to Noise Ratio (PSNR) and Compression Ratios of the original images and reconstructed images. The results obtained by the proposed method have been found to be significantly better than those obtained by the above mentioned algorithms.

Entropy-based thresholding techniques are quite popular and effective for image segmentation. Among different entropy-based techniques, minimum cross-entropy thresholding (MCET) has received wide attention in the field of image segmentation. Considering the high time complexity of MCET technique for multilevel thresholding, recursive approach to reducing its computational cost is highly desired. To reduce the complexity, further optimization techniques can be applied to find optimal multilevel threshold values. In this paper, a novel improved particle swarm optimization (IPSO)-based multilevel thresholding algorithm is proposed to search the near-optimal MCET thresholds. The general PSO algorithm often suffers from premature convergence problem which has been addressed in the IPSO by decomposing a high-dimensional swarm into several one-dimensional swarms, and then premature convergence is removed from each one-dimensional swarm. The proposed technique is applied to the set of grayscale images, and the experimental results infer that it produces better MCET optimal threshold values at a higher and faster convergence rate. The qualitative and quantitative results are compared with existing optimization techniques like modified artificial bee colony, Cuckoo search, Firefly, particle swarm optimization, and genetic algorithm. It has been observed that the proposed technique performs better in terms of producing better fitness value, less CPU time as quantitative measurements, and effective misclassification error, peak signal-to-noise ratio, feature similarity index measurement, complex wavelet structural similarity index measurement values as qualitative measurements compared to other considered state-of-the-art methods.