An evolutionary gray gradient algorithm for multilevel thresholding of brain MR images using soft computing techniques

Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

Adaptive entropy theory polymerization method for path optimization in edge computing educational systems

In this paper, through the edge computing application path, the educational evaluation system was optimized using the adaptive entropy theory polymerization method which based on applying the path. By adding multiple constraints to filter out nodes and educational evaluation edges that do not meet the requirements, the improved algorithm is used to optimize the redundant paths to avoid loops and node detour problem. To improve the accuracy of education evaluation and evaluation, ensure the load balance in the domain, and solve the problems of single evaluation attribute and high overlap of education evaluation paths. This paper proposes a multi-attribute education evaluation model that refines the evaluation attributes of education evaluation and uses analytic hierarchy process perform weight distribution. The algorithm can improve the accuracy of the evaluation of the education evaluation system while ensuring the computational efficiency, and can ensure the load balance within the domain, and improve the network survival time.

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.

Brain Tumor Detection Based on Multilevel 2D Histogram Image Segmentation Using DEWO Optimization Algorithm

Brain tumor detection from magnetic resonance (MR)images is a tedious task but vital for early prediction of the disease which until now is solely based on the experience of medical practitioners. Multilevel image segmentation is a computationally simple and efficient approach for segmenting brain MR images. Conventional image segmentation does not consider the spatial correlation of image pixels and lacks better post-filtering efficiency. This study presents a Renyi entropy-based multilevel image segmentation approach using a combination of differential evolution and whale optimization algorithms (DEWO) to detect brain tumors. Further, to validate the efficiency of the proposed hybrid algorithm, it is compared with some prominent metaheuristic algorithms in recent past using between-class variance and the Tsallis entropy functions. The proposed hybrid algorithm for image segmentation is able to achieve better results than all the other metaheuristic algorithms in every entropy-based segmentation performed on brain MR images.

An Improved Marine Predators Algorithm With Fuzzy Entropy for Multi-Level Thresholding: Real World Example of COVID-19 CT Image Segmentation

Medical imaging techniques play a critical role in diagnosing diseases and patient healthcare. They help in treatment, diagnosis, and early detection. Image segmentation is one of the most important steps in processing medical images, and it has been widely used in many applications. Multi-level thresholding (MLT) is considered as one of the simplest and most effective image segmentation techniques. Traditional approaches apply histogram methods; however, these methods face some challenges. In recent years, swarm intelligence methods have been leveraged in MLT, which is considered an NP-hard problem. One of the main drawbacks of the SI methods is when searching for optimum solutions, and some may get stuck in local optima. This because during the run of SI methods, they create random sequences among different operators. In this study, we propose a hybrid SI based approach that combines the features of two SI methods, marine predators algorithm (MPA) and moth-?ame optimization (MFO). The proposed approach is called MPAMFO, in which, the MFO is utilized as a local search method for MPA to avoid trapping at local optima. The MPAMFO is proposed as an MLT approach for image segmentation, which showed excellent performance in all experiments. To test the performance of MPAMFO, two experiments were carried out. The first one is to segment ten natural gray-scale images. The second experiment tested the MPAMFO for a real-world application, such as CT images of COVID-19. Therefore, thirteen CT images were used to test the performance of MPAMFO. Furthermore, extensive comparisons with several SI methods have been implemented to examine the quality and the performance of the MPAMFO. Overall experimental results confirm that the MPAMFO is an efficient MLT approach that approved its superiority over other existing methods.

Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.