Multilevel Thresholding for Image Segmentation Using an Improved Electromagnetism Optimization Algorithm

Image segmentation is considered one of the most important tasks in image processing, which has several applications in different areas such as; industry agriculture, medicine, etc. In this paper, we develop the electromagnetic optimization (EMO) algorithm based on levy function, EMO-levy, to enhance the EMO performance for determining the optimal multi-level thresholding of image segmentation. In general, EMO simulates the mechanism of attraction and repulsion between charges to develop the individuals of a population. EMO takes random samples from search space within the histogram of image, where, each sample represents each particle in EMO. The quality of each particle is assessed based on Otsu’s or Kapur objective function value. The solutions are updated using EMO operators until determine the optimal objective functions. Finally, this approach produces segmented images with optimal values for the threshold and a few number of iterations. The proposed technique is validated using different standard test images. Experimental results prove the effectiveness and superiority of the proposed algorithm for image segmentation compared with well-known optimization methods

Tissue segmentation using medical image processing chain optimization

Surveyed literature shows many segmentation algorithms using different types of optimization methods. These methods were used to minimize or maximize objective functions of entropy, similarity, clustering, contour, or thresholding. These specially developed functions target specific feature or step in the presented segmentation algorithms. To the best of our knowledge, this thesis is the first research work that uses an optimizer to build and optimize parameters of a full sequence of image processing chain. This thesis presents a universal algorithm that uses three images and their corresponding gold images to train the framework. The optimization algorithm explores the search space for the best sequence of the image processing chain to segment the targeted feature. Experiments indicate that using differential evolution to build Image processing chain (IPC) out of forty-five algorithms increases the segmentation performance of basic thresholding algorithms ranging from 2% to 78%

Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.Sponsored by: IEEE Geoscience and Remote Sensing SocietyRitrýnt tímaritPeer reviewedPre prin

Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization

The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time to find the optimum thresholds of multilevel thresholding which evaluated on several grayscale images. A detailed comparison analysis with other multilevel thresholding based techniques namely particle swarm optimization (PSO), iterative particle swarm optimization (IPSO), and genetic algorithms (GA), From the experiments, Modified particle swarm optimization (MoPSO) produces better performance compared to the other methods in terms of fitness value, robustness and convergence. Therefore, it can be concluded that MoPSO is a good approach in finding the optimal threshold value

Detection of Diabetic Retinopathy Through Identification of Blood Vessel Thickness Using-FOFF

Diabetes is one of the main problems in today's medical industry. Identification and diagnosis of the eye condition brought on by diabetes' elevated blood sugar levels are crucial. The motive of the work is to present a useful technique for system diagnosis utilizing a retinal fundus picture. A four-stage implementation paradigm is created in this study. Initially the input image is pre-processed, secondly blood vessel segmentation is performed; thirdly feature extraction and finally classification. In this paper an effective methodology is utilized by introducing meta heuristic algorithm. In the entire process, blood vessel segmentation (BVS)contributes a crucial part in DR detection for which a firefly optimized frangi filter (FOFF) is designed in this paper. The process of categorization comes last. The classifiers K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are employed. The performance of system is evaluated by computing the accuracy and precision. The results are compared between the two utilized classifiers. The SVM performance is good with an accuracy of 95.5% and KNN having an accuracy of 91.6%

Cooperative Swarm Intelligence Algorithms for Adaptive Multilevel Thresholding Segmentation of COVID-19 CT-Scan Images

The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounter some limitations. Now, swarm intelligence inspired meta-heuristic algorithms have been applied to resolve MLT, deemed an NP-hard optimization task. Despite the advantages of using meta-heuristics to solve global optimization tasks, each approach has its own drawbacks. However, the common flaw for most meta-heuristic algorithms is that they are unable to maintain the diversity of their population during the search, which means they might not always converge to the global optimum. This study proposes a cooperative swarm intelligence-based MLT image segmentation approach that hybridizes the advantages of parallel meta-heuristics and MLT for developing an efficient image segmentation method for COVID-19 CT images. An efficient cooperative model-based meta-heuristic called the CPGH is developed based on three practical algorithms: particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawks optimization (HHO). In the cooperative model, the applied algorithms are executed concurrently, and a number of potential solutions are moved across their populations through a procedure called migration after a set number of generations. The CPGH model can solve the image segmentation problem using MLT image segmentation. The proposed CPGH is evaluated using three objective functions, cross-entropy, Otsu’s, and Tsallis, over the COVID-19 CT images selected from open-sourced datasets. Various evaluation metrics covering peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and universal quality image index (UQI) were employed to quantify the segmentation quality. The overall ranking results of the segmentation quality metrics indicate that the performance of the proposed CPGH is better than conventional PSO, GWO, and HHO algorithms and other state-of-the-art methods for MLT image segmentation. On the tested COVID-19 CT images, the CPGH offered an average PSNR of 24.8062, SSIM of 0.8818, and UQI of 0.9097 using 20 thresholds

A Novel Plant Root Foraging Algorithm for Image Segmentation Problems

This paper presents a new type of biologically-inspired global optimization methodology for image segmentation based on plant root foraging behavior, namely, artificial root foraging algorithm (ARFO). The essential motive of ARFO is to imitate the significant characteristics of plant root foraging behavior including branching, regrowing, and tropisms for constructing a heuristic algorithm for multidimensional and multimodal problems. A mathematical model is firstly designed to abstract various plant root foraging patterns. Then, the basic process of ARFO algorithm derived in the model is described in details. When tested against ten benchmark functions, ARFO shows the superiority to other state-of-the-art algorithms on several benchmark functions. Further, we employed the ARFO algorithm to deal with multilevel threshold image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the suitability of the proposed method for solving such problem

Soft computing applied to optimization, computer vision and medicine

Artificial intelligence has permeated almost every area of life in modern society, and its significance continues to grow. As a result, in recent years, Soft Computing has emerged as a powerful set of methodologies that propose innovative and robust solutions to a variety of complex problems. Soft Computing methods, because of their broad range of application, have the potential to significantly improve human living conditions. The motivation for the present research emerged from this background and possibility. This research aims to accomplish two main objectives: On the one hand, it endeavors to bridge the gap between Soft Computing techniques and their application to intricate problems. On the other hand, it explores the hypothetical benefits of Soft Computing methodologies as novel effective tools for such problems. This thesis synthesizes the results of extensive research on Soft Computing methods and their applications to optimization, Computer Vision, and medicine. This work is composed of several individual projects, which employ classical and new optimization algorithms. The manuscript presented here intends to provide an overview of the different aspects of Soft Computing methods in order to enable the reader to reach a global understanding of the field. Therefore, this document is assembled as a monograph that summarizes the outcomes of these projects across 12 chapters. The chapters are structured so that they can be read independently. The key focus of this work is the application and design of Soft Computing approaches for solving problems in the following: Block Matching, Pattern Detection, Thresholding, Corner Detection, Template Matching, Circle Detection, Color Segmentation, Leukocyte Detection, and Breast Thermogram Analysis. One of the outcomes presented in this thesis involves the development of two evolutionary approaches for global optimization. These were tested over complex benchmark datasets and showed promising results, thus opening the debate for future applications. Moreover, the applications for Computer Vision and medicine presented in this work have highlighted the utility of different Soft Computing methodologies in the solution of problems in such subjects. A milestone in this area is the translation of the Computer Vision and medical issues into optimization problems. Additionally, this work also strives to provide tools for combating public health issues by expanding the concepts to automated detection and diagnosis aid for pathologies such as Leukemia and breast cancer. The application of Soft Computing techniques in this field has attracted great interest worldwide due to the exponential growth of these diseases. Lastly, the use of Fuzzy Logic, Artificial Neural Networks, and Expert Systems in many everyday domestic appliances, such as washing machines, cookers, and refrigerators is now a reality. Many other industrial and commercial applications of Soft Computing have also been integrated into everyday use, and this is expected to increase within the next decade. Therefore, the research conducted here contributes an important piece for expanding these developments. The applications presented in this work are intended to serve as technological tools that can then be used in the development of new devices

A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems

Image segmentation is considered a crucial step required for image analysis and research. Many techniques have been proposed to resolve the existing problems and improve the quality of research, such as region-based, threshold-based, edge-based, and feature-based clustering in the literature. The researchers have moved toward using the threshold technique due to the ease of use for image segmentation. To find the optimal threshold value for a grayscale image, we improved and used a novel meta-heuristic equilibrium algorithm to resolve this scientific problem. Additionally, our improved algorithm has the ability to enhance the accuracy of the segmented image for research analysis with a significant threshold level. The performance of our algorithm is compared with seven other algorithms like whale optimization algorithm, bat algorithm, sine–cosine algorithm, salp swarm algorithm, Harris hawks algorithm, crow search algorithm, and particle swarm optimization. Based on a set of well-known test images taken from Berkeley Segmentation Dataset, the performance evaluation of our algorithm and well-known algorithms described above has been conducted and compared. According to the independent results and analysis of each algorithm, our algorithm can outperform all other algorithms in fitness values, peak signal-to-noise ratio metric, structured similarity index metric, maximum absolute error, and signal-to-noise ratio. However, our algorithm cannot outperform some algorithms in standard deviation values and central processing unit time with the large threshold levels observed