Modified Discrete Grey Wolf Optimizer Algorithm for Multilevel Image Thresholding

The computation of image segmentation has become more complicated with the increasing number of thresholds, and the option and application of the thresholds in image thresholding fields have become an NP problem at the same time. The paper puts forward the modified discrete grey wolf optimizer algorithm (MDGWO), which improves on the optimal solution updating mechanism of the search agent by the weights. Taking Kapur’s entropy as the optimized function and based on the discreteness of threshold in image segmentation, the paper firstly discretizes the grey wolf optimizer (GWO) and then proposes a new attack strategy by using the weight coefficient to replace the search formula for optimal solution used in the original algorithm. The experimental results show that MDGWO can search out the optimal thresholds efficiently and precisely, which are very close to the result examined by exhaustive searches. In comparison with the electromagnetism optimization (EMO), the differential evolution (DE), the Artifical Bee Colony (ABC), and the classical GWO, it is concluded that MDGWO has advantages over the latter four in terms of image segmentation quality and objective function values and their stability

Application of metaheuristic optimization algorithms for image registration in mobile robot visual control

Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses

Application of metaheuristic optimization algorithms for image registration in mobile robot visual control

Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses

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

HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images

Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 — sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics

Виділення ознак профілів зображення обличчя для систем розпізнавання

The object of research is the algorithm of piecewise linear approximation when applying it to the selection of facial features and compression of its images. One of the problem areas is to obtain the optimal ratio of the degree of compression and accuracy of image reproduction, as well as the accuracy of the obtained facial features, which can be used to search for people in databases. The main characteristics of the image of the face are the coordinates and size of the eyes, mouth, nose and other objects of attention. Dimensions, distances between them, as well as their relationship also form a set of characteristics. A piecewise linear approximation algorithm is used to identify and determine these features. First, it is used to approximate the image of the face to obtain a graph of the silhouette from right to left and, secondly, to approximate fragments of the face to obtain silhouettes of the face from top to bottom. The purpose of the next stage is to implement multilevel segmentation of the approximated images to cover them with rectangles of different intensity. Due to their shape they are called barcodes. These three stages of the algorithm the faces are represented by two barcode images are vertical and horizontal. This material is used to calculate facial features. The medium intensity function in a row or column is used to form an approximation object and as a tool to measure the values of facial image characteristics. Additionally, the widths of barcodes and the distances between them are calculated. Experimental results with faces from known databases are presented. A piecewise linear approximation is used to compress facial images. Experiments have shown how the accuracy of the approximation changes with the degree of compression of the image. The method has a linear complexity of the algorithm from the number of pixels in the image, which allows its testing for large data. Finding the coordinates of a synchronized object, such as the eyes, allows calculating all the distances between the objects of attention on the face in relative form. The developed software has control parameters for conducting research.Об'єктом дослідження є алгоритм кусково-лінійної апроксимації за застосування його до виділення ознак та стиснення зображень обличчя. Одним з проблемних місць є отримання оптимального співвідношення ступеня стиснення та точності відтворення зображення, а також точності отриманих ознак обличчя, які можна застосувати для пошуку осіб у базах даних. Основними характеристиками зображення обличчя є координати та розмір очей, рота, носа та інших об'єктів уваги. Розміри, відстані між ними, а також їх відношення теж утворюють набір характеристик. Для виявлення та визначення цих особливостей використовують алгоритм кусково-лінійної апроксимації. По-перше, його застосовують для апроксимації зображення обличчя, щоб отримати графік силуету справа наліво і, по-друге, для апроксимованих фрагментів обличчя, щоб отримати силуети обличчя зверху вниз. Метою наступного етапу є реалізація багаторівневої сегментації апроксимованих зображень, щоб покрити їх прямокутниками різної інтенсивності. Завдяки своїй формі вони названі штрих-кодами. Ці три етапи алгоритму обличчя подаються двома зображеннями штрих-кодів: вертикальним і горизонтальним. За цим матеріалом розраховують ознаки обличчя. Функцію середньої інтенсивності в рядку або стовпці використовують для формування об'єкта апроксимації та як інструмент для вимірювання значень характеристик зображення обличчя. Додатково розраховують ширини штрих-кодів та відстані між ними. Наведено експериментальні результати з обличчями з відомих баз даних. Кусково-лінійну апроксимацію використано для стиснення зображень обличчя. Експериментами показано, як змінюється точність апроксимації залежно від ступеня стиснення зображення. Метод має лінійну складність алгоритму залежно від кількості пікселів у зображенні, що дає змогу його тестувати для великих даних. Знаходження координат об'єкта синхронізації, наприклад очей, дає змогу обчислити всі відстані між об'єктами уваги на обличчі у відносній формі. Розроблене програмне забезпечення має параметри керування для виконання досліджень

Algoritmos baseados em inteligência de enxames aplicados à multilimiarização de imagens

Orientador: Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 20/08/2018Inclui referências: p.117-122Área de concentração: Sistemas EletrônicosResumo: O processamento de imagens é uma área que cresce à medida que as tecnologias de geração e armazenamento de informações digitais evoluem. Uma das etapas iniciais do processamento de imagem é a segmentação, onde a multilimiarização é uma das técnicas de segmentação mais simples. Um focorelevante de pesquisa nesta área é o projeto de abordagens visando a separação de diferentes objetos na imagem em grupos, por meio de limiares, para facilitar assim a interpretação da informação contida na imagem. Uma imagem perde informação, ou entropia, quando é limiarizada. A equação de limiarização multiníveis de Kapur calcula, a partir dos limiares escolhidos, qual a quantidade de informação que uma imagem apresentará após a limiarização. Assim, pela maximização da equação de multimiliarização de Kapur, é possível determinar os limiares que retornam uma imagem com valor maior de entropia. Quanto maior a quantidade de limiares, maior a dificuldade para encontrar a melhor solução, devido ao aumento significativo da quantidade de possíveis soluções. O objetivo desta dissertação é de apresentar um estudo comparativodecinco algoritmos de otimização (meta-heurísticas de otimização)da inteligência de enxame, incluindo Otimização por Enxame de Partículas (PSO), Otimização por Enxame de Partículas Darwiniano (DPSO), Otimização por Enxame de Partículas Darwiniano de Ordem Fracionária (FO-DPSO), Otimizador baseado no comportamento dos Lobos-cinza (GWO) e Otimizador inspirado no comportamento da Formiga-leão (ALO), de forma a avaliarqual deles obtém a melhor solução e convergência em termos da função objetivo relacionada a entropia da imagem. Uma contribuição desta dissertação é a aplicação de diferentes meta-heurísticas de otimização ao problema de multilimiarização de imagens, assim como o estudo do impacto das suas variáveis de controle (hiperparâmetros) para o problema em questão.Nesta dissertação são apresentados resultados paraquatro imagens diferentes, sendo duas imagens registradas por satélite (Rio Hunza e Yellowstone) e outras duas imagens teste (benchmark) obtidas do Centro de Engenharia Elétrica e Ciência da Computação do MIT (Massachussetts Institute of Technology). Os resultados são comparados considerando a média e o desvio padrão da entropia de cada imagem resultante. Com base nos resultados obtidos conclui-se que o algoritmo mais indicado para o problema de multilimiarização de imagens dos avaliados é o GWO, pelo seu desempenho superior em relação aos outros algoritmos e pelas entropias das imagens resultantes serem satisfatórias. Palavras-chave: Segmentação de imagens. Multilimiarização. Inteligência de enxames. Otimização por enxame de partículas. Otimizador dos lobos-cinza. Otimizador formiga-leão.Abstract: Image processing is a field that grows as digital information storage and generation technologies evolution. One of the initial stages of image processing is segmentation procedure, where the multi level thresholding is one of the simplest segmentation approaches. A relevant research objective in this field is the design of approaches aimed at separating different objects in the image into groups, through thresholds, to facilitate the interpretation of the information contained in the image. An image loses information, or entropy, when it is thresholded. The Kapur multilevel thresholding equation calculates, from the chosen thresholds, how much information an image will present after the thresholding. Thus, by the maximization of the Kapur multilevel limiarization equation, it is possible to determine the thresholds that return an image with a larger value of entropy. The higher the amount of thresholds, the greater the difficulty in finding the best solution, due to the significant increase in the quantity of possible solutions. The objective of this dissertation is to present a comparative study between fiveoptimization metaheuristics of the swarm intelligence field, including Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), Fractional Order Darwinian Particle Swarm Optimization (FO-DPSO), Grey Wolf Optimizer (GWO) and the Ant lion behavioral optimizer (ALO), in order to identify which one gets the best solution and convergence in terms of the objective function and the entropy of the image. A contribution of this dissertation is the application of different optimization metaheuristics to the problem of multilimizing of images, as well as the study of the impact of its control variables (hyperparameters) on the problem in question. Experiments are conducted with four images, two images being recorded by satellite (Hunza River and Yellowstone) and two other test(benchmark) images obtained from MIT's (Massachussetts Institute of Technology) Electrical Engineering and Computer Science Center. The results are compared considering the mean and standard deviation values of each resulting image entropy.Based on the results obtained it is concluded that the most suitable algorithm for the problem of multilevel thresholding of images is the GWO, for its superior performance in relation to the other tested algorithms and satisfactory entropies of the resulting images. Key-words: Image segmentation. Multilevel thresholding. Kapur's entropy. Swarm intelligence. Particle swarm optimization. Grey wolf optimizer. Ant lion optimizer

Spatial fuzzy c-mean sobel algorithm with grey wolf optimizer for MRI brain image segmentation

Segmentation is the process of dividing the original image into multiple sub regions called segments in such a way that there is no intersection between any two regions. In medical images, the segmentation is hard to obtain due to the intensity similarity among various regions and the presence of noise in medical images. One of the most popular segmentation algorithms is Spatial Fuzzy C-means (SFCM). Although this algorithm has a good performance in medical images, it suffers from two issues. The first problem is lack of a proper strategy for point initialization step, which must be performed either randomly or manually by human. The second problem of SFCM is having inaccurate segmented edges. The goal of this research is to propose a robust medical image segmentation algorithm that overcomes these weaknesses of SFCM for segmenting magnetic resonance imaging (MRI) brain images with less human intervention. First, in order to find the optimum initial points, a histogram based algorithm in conjunction with Grey Wolf Optimizer (H-GWO) is proposed. The proposed H-GWO algorithm finds the approximate initial point values by the proposed histogram based method and then by taking advantage of GWO, which is a soft computing method, the optimum initial values are found. Second, in order to enhance SFCM segmentation process and achieve higher accurate segmented edges, an edge detection algorithm called Sobel was utilized. Therefore, the proposed hybrid SFCM-Sobel algorithm first finds the edges of the original image by Sobel edge detector algorithm and finally extends the edges of SFCM segmented images to the edges that are detected by Sobel. In order to have a robust segmentation algorithm with less human intervention, the H-GWO and SFCM-Sobel segmentation algorithms are integrated to have a semi-automatic robust segmentation algorithm. The results of the proposed H-GWO algorithms show that optimum initial points are achieved and the segmented images of the SFCM-Sobel algorithm have more accurate edges as compared to recent algorithms. Overall, quantitative analysis indicates that better segmentation accuracy is obtained. Therefore, this algorithm can be utilized to capture more accurate segmented in images in the era of medical imaging