A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy

Many countries are challenged by the medical resources required for COVID-19 detection which necessitates the development of a low-cost, rapid tool to detect and diagnose the virus effectively for a large numbers of tests. Although a chest X-Ray scan is a useful candidate tool the images generated by the scans must be analyzed accurately and quickly if large numbers of tests are to be processed. COVID-19 causes bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. In this work, we aim to extract rapidly from chest X-Ray images the similar small regions that may contain the identifying features of COVID-19. This paper therefore proposes a hybrid COVID-19 detection model based on an improved marine predators algorithm (IMPA) for X-Ray image segmentation. The ranking-based diversity reduction (RDR) strategy is used to enhance the performance of the IMPA to reach better solutions in fewer iterations. RDR works on finding the particles that couldn't find better solutions within a consecutive number of iterations, and then moving those particles towards the best solutions so far. The performance of IMPA has been validated on nine chest X-Ray images with threshold levels between 10 and 100 and compared with five state-of-art algorithms: equilibrium optimizer (EO), whale optimization algorithm (WOA), sine cosine algorithm (SCA), Harris-hawks algorithm (HHA), and salp swarm algorithms (SSA). The experimental results demonstrate that the proposed hybrid model outperforms all other algorithms for a range of metrics. In addition, the performance of our proposed model was convergent on all numbers of thresholds level in the Structured Similarity Index Metric (SSIM) and Universal Quality Index (UQI) metrics.</p

Hyperspectral Image Classification based on Dimensionality Reduction and Swarm Optimization

Hyperspectral images have high dimensions, making it difficult to determine accurate and efficient image segmentation algorithms. Dimension reduction data is done to overcome these problems. In this paper we use Discriminant independent component analysis (DICA). The accuracy and efficiency of the segmentation algorithm used will affect final results of image classification. In this paper a new method of multilevel thresholding is introduced for segmentation of hyperspectral images. A method of swarm optimization approach, namely Darwinian Particle Swarm Optimization (DPSO) is used to find n-1 optimal m-level threshold on a given image. A new classification image approach based on Darwinian particle swarm optimization (DPSO) and support vector machine (SVM) is used in this paper. The method introduced in this paper is compared to existing approach. The results showed that the proposed method was better than the standard SVM in terms of classification accuracy namely average accuracy (AA), overall accuracy (OA and Kappa index (K)

Optimization Methods for Image Thresholding: A review

Setting a border with the proper gray level in processing images to separate objects from their backgrounds is crucial. One of the simplest and most popular methods of segmenting pictures is histogram-based thresholding. Thresholding is a common technique for image segmentation because of its simplicity. Thresholding is used to separate the Background of the image from the Foreground. There are many methods of thresholding. This paper aims to review many previous studies and mention the types of thresholding. It includes two types: the global and local thresholding methods and each type include a group of methods. The global thresholding method includes (the Otsu method, Kapur's entropy method, Tsallis entropy method, Hysteresis method, and Fuzzy entropy method), and the local thresholding method includes ( Ni-Black method and Bernsen method). The optimization algorithms(Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Modified Grasshopper Optimization, Firefly Algorithm, Cuckoo Search, Tabu Search Algorithm, Simulated Annealing, and Jaya Algorithm) used along with thresholding methods are also illustrated

Multilevel Thresholding Segmentation based on Otsu’s Method and Autonomous Groups Particle Swarm Optimization for Multispectral Image

Segmentation is a process of division of images into certain regions based on certain similarities. Multispectral image consists of several bands with high dimensions, requiring a different method with the problem of low-dimensional images. Multilevel thresholding problems based on Otsu criteria are discussed in this paper. One disadvantage of the Otsu method is that computing time increases exponentially according to the number of thresholding dimensions. In this paper, the Particle Swarm Optimization (PSO) algorithm combined with the Otsu Method called multilevel thresholding Autonomous Groups Particles Swarm Optimization (MAGPSO) is proposed to reduce the two problems of PSO entrapment in the local minima and the slow rate of convergence in solving high dimensional problems. MAGPSO is used for multilevel thresholding image segmentation. The performance of MAGPSO is compared with standard PSO on three natural images. The parameters used to compare the performance of MAGPSO and PSO are the best fitness value, optimal threshold obtained from each algorithm and the measurement of the quality of segmentation results, namely: SSIM, PSNR, and MSE. From the experimental results show that MAGPSO is better when compared to PSO in image segmentation, in terms of the resulting fitness value and higher SSIM and PNSR values

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

A Study on RGB Image Multi-Thresholding using Kapur/Tsallis Entropy and Moth-Flame Algorithm

In the literature, a considerable number of image processing and evaluation procedures are proposed and implemented in various domains due to their practical importance. Thresholding is one of the pre-processing techniques, widely implemented to enhance the information in a class of gray/RGB class pictures. The thresholding helps to enhance the image by grouping the similar pixels based on the chosen thresholds. In this research, an entropy assisted threshold is implemented for the benchmark RGB images. The aim of this work is to examine the thresholding performance of well-known entropy functions, such as Kapur’s and Tsallis for a chosen image threshold. This work employs a Moth-Flame-Optimization (MFO) algorithm to support the automatic identification of the finest threshold (Th) on the benchmark RGB image for a chosen threshold value (Th=2,3,4,5). After getting the threshold image, a comparison is performed against its original picture and the necessary Picture-Quality-Values (PQV) is computed to confirm the merit of the proposed work. The experimental investigation is demonstrated using benchmark images with various dimensions and the outcome of this study confirms that the MFO helps to get a satisfactory result compared to the other heuristic algorithms considered in this study

Algoritmos metaheurísticos para la segmentación de imágenes

Uno de los temas más tratados en la comunidad de procesamiento de imágenes es la segmentación, consistente en obtener estructuras subyacentes para facilitar su interpretación, por ejemplo, obtener bordes o agrupaciones de píxeles que forman regiones con alguna propiedad. Dado que se utiliza como un paso de preprocesamiento antes de las tareas de visión por computador de alto nivel, como el reconocimiento de objetos y la representación de imágenes, se han propuesto diversos enfoques para la segmentación, que se centran en la mejora de la calidad de los procesos aplicados para conseguir los mejores resultados posibles. Sin embargo, en muchos casos el coste computacional de estas técnicas puede ser elevado, limitando su uso. En el área de la optimización global se han propuesto una gran cantidad de algoritmos metaheurísticos (AM) para resolver problemas complejos de ingeniería en un tiempo razonable. Los AMs son algoritmos de búsqueda estocásticos que utilizan reglas o heurísticas aplicables a cualquier problema para acelerar su convergencia a soluciones cercanas al óptimo. Es común observar que los AMs emulan procesos y comportamientos inspirados por mecanismos presentes en la naturaleza, como la evolución..