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

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

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

An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing

Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and q-thresholding algorithm. In DCS-GWO, the grey wolves’ positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO is efficient in finding global optimum solution. The simulation results illustrate that DCS-GWO has better recovery performance than previous greedy pursuit algorithms at the expense of computational complexity

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

Comparison of Recent Meta-Heuristic Optimization Algorithms Using Different Benchmark Functions

Meta-heuristic optimization algorithms are used in many application areas to solve optimization problems. In recent years, meta-heuristic optimization algorithms have gained importance over deterministic search algorithms in solving optimization problems. However, none of the techniques are equally effective in solving all optimization problems. Therefore, researchers have focused on either improving current meta-heuristic optimization techniques or developing new ones. Many alternative meta-heuristic algorithms inspired by nature have been developed to solve complex optimization problems. It is important to compare the performances of the developed algorithms through statistical analysis and determine the better algorithm. This paper compares the performances of sixteen meta-heuristic optimization algorithms (AWDA, MAO, TSA, TSO, ESMA, DOA, LHHO, DSSA, LSMA, AOSMA, AGWOCS, CDDO, GEO, BES, LFD, HHO) presented in the literature between 2021 and 2022. In this context, various test functions, including single-mode, multi-mode, and fixed-size multi-mode benchmark functions, were used to evaluate the efficiency of the algorithms used

VGG19+CNN: Deep Learning-Based Lung Cancer Classification with Meta-Heuristic Feature Selection Methodology

Lung illnesses are lung-affecting illnesses that harm the respiratory mechanism. Lung cancer is one of the major causes of death in humans internationally. Advance diagnosis could optimise survivability amongst humans. This remains feasible to systematise or reinforce the radiologist for cancer prognosis. PET and CT scanned images can be used for lung cancer detection. On the whole, the CT scan exhibits importance on the whole and functions as a comprehensive operation in former cancer prognosis. Thus, to subdue specific faults in choosing the feature and optimise classification, this study employs a new revolutionary algorithm called the Accelerated Wrapper-based Binary Artificial Bee Colony algorithm (AWBABCA) for effectual feature selection and VGG19+CNN for classifying cancer phases. The morphological features will be extracted out of the pre-processed image; next, the feature or nodule related to the lung that possesses a significant impact on incurring cancer will be chosen, and for this intention, herein AWBABCA has been employed. The chosen features will be utilised for cancer classification, facilitating a great level of strength and precision. Using the lung dataset to do an experimental evaluation shows that the proposed classifier got the best accuracy, precision, recall, and f1-score