scholarly journals Multi-Level Image Thresholding Based on Modified Spherical Search Optimizer and Fuzzy Entropy

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 328 ◽  
Author(s):  
Husein S Naji Alwerfali ◽  
Mohammed A. A. Al-qaness ◽  
Mohamed Abd Elaziz ◽  
Ahmed A. Ewees ◽  
Diego Oliva ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Fitness Function ◽  
Fuzzy Entropy ◽  
Threshold Values ◽  
Traditional Methods ◽  
Thresholding Method ◽  
Segmentation Methods ◽  
Sine Cosine Algorithm ◽  
Multi Level

Multi-level thresholding is one of the effective segmentation methods that have been applied in many applications. Traditional methods face challenges in determining the suitable threshold values; therefore, metaheuristic (MH) methods have been adopted to solve these challenges. In general, MH methods had been proposed by simulating natural behaviors of swarm ecosystems, such as birds, animals, and others. The current study proposes an alternative multi-level thresholding method based on a new MH method, a modified spherical search optimizer (SSO). This was performed by using the operators of the sine cosine algorithm (SCA) to enhance the exploitation ability of the SSO. Moreover, Fuzzy entropy is applied as the main fitness function to evaluate the quality of each solution inside the population of the proposed SSOSCA since Fuzzy entropy has established its performance in literature. Several images from the well-known Berkeley dataset were used to test and evaluate the proposed method. The evaluation outcomes approved that SSOSCA showed better performance than several existing methods according to different image segmentation measures.

scholarly journals Multi-Level Thresholding for Image Segmentation With Swarm Optimization Algorithms

2017 ◽  
Vol 3 (3) ◽  
pp. 1 ◽  
Author(s):  
Murat Karakoyun ◽  
Nurdan Akhan Baykan ◽  
Mehmet Hacibeyoglu
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Threshold Value ◽  
Threshold Values ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Multi Level

Image segmentation is an important problem for image processing. The image processing applications are generally affectedfromthe segmentation success. There is noany image segmentation method which gives good results for all sorts of images. That’s why there are many approaches and methods forimage segmentationin the literature. And one of the most used is the thresholding technique. Thresholding techniques can be categorized into two topics: bi-level and multi-level thresholding. Bi-level thresholding technique has one threshold value which separates the image into two groups. However, multi-level thresholding technique uses n threshold values where n greater than one. In this paper, two swarm optimization algorithms (Particle Swarm Optimization, PSO and Cat Swarm Optimization, CSO) are applied on finding the optimum threshold values for the multi-level thresholding. In literature, there are some minimization or maximization functions to find the best threshold values for thresholding problem. Some of these methods are: Tsalli’s Entropy, Kapur’s Entropy, Renyi’s Entropy, Otsu’s Method (within class variance/between class variance), the Minimum Cross Entropy Thresholding (MCET) etc.In this work, Otsu’s (within class variance) method, which is one of these popular functions,is used as the fitness function of algorithms.In the experiments, five real images are segmented by usingParticle Swarm Algorithm and Cat Swarm Optimization Algorithms. The performances of the swarm algorithms on multi-level thresholding problem arecompared with Peak Signal-to-Noise Ratio (PSNR) and fitness function (FS) values. As a result, the PSO yields better performance than CSO.

NEW APPROACH TO MULTI-LEVEL PROCESSOR SCHEDULING

2010 ◽  
Vol 19 (03) ◽  
pp. 335-346 ◽  
Author(s):  
SAMANEH HOSSEINI SEMNANI ◽  
KAMRAN ZAMANIFAR
Keyword(s):  
Fitness Function ◽  
Systems Design ◽  
Heuristic Function ◽  
Problem Space ◽  
New Approach ◽  
Processor Scheduling ◽  
Quantum Time ◽  
Multi Level ◽  
Evaluation Parameters

The problem of finding the best quantum time in multi-level processor scheduling is addressed in this paper. Processor scheduling is one of the most important issues in operating systems design. Different schedulers are introduced to solve this problem. In one scheduling approach, processes are placed in different queues according to their properties, and the processor allocates time to each queue iteratively. One of the most important parameters of a processor's efficiency in this approach is the amount of time slices associated to each processor queue. In this paper, an ant colony optimization (ACO) algorithm is presented to solve the problem of finding appropriate time slices to assign to each processor queue. In this technique, each ant tries to find an appropriate scheduling. Ant algorithm searches the problem space to find the best scheduling. The quality of each ant's solution is evaluated using a new fitness function. This fitness function is designed according to the evaluation parameters of each processor queue and also according to the queue theory's relations. Also a heuristic function is presented which prompts ant to select better solutions. Computational tests are presented and the comparisons made with genetic algorithm (GA) and particle swarm optimization (PSO) algorithms which try to solve same problem. The results show the efficiency of this algorithm.

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

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 125306-125330 ◽  
Author(s):  
Mohamed Abd Elaziz ◽  
Ahmed A. Ewees ◽  
Dalia Yousri ◽  
Husein S. Naji Alwerfali ◽  
Qamar A. Awad ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Real World ◽  
Fuzzy Entropy ◽  
Ct Image ◽  
Marine Predators ◽  
Multi Level

Multi-level Image Segmentation using Randomized Spiral-based Whale Optimization Algorithm

2020 ◽  
Vol 14 ◽  
Author(s):  
Basu Dev Shivahare ◽  
S.K. Gupta
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Objective Evaluation ◽  
Whale Optimization Algorithm ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Whale Optimization ◽  
Novel Variant ◽  
Multiple Regions ◽  
Multi Level

Abstract: Segmenting an image into multiple regions is a pre-processing phase of computer vision. For the same, determining an optimal set of thresholds is challenging problem. This paper introduces a novel multi-level thresholding based image segmentation method. The presented method uses a novel variant of whale optimization algorithm to determine the optimal thresholds. For experimental validation of the proposed variant, twenty-three benchmark functions are considered. To analysis the efficacy of new multi-level image segmentation method, images from Berkeley Segmentation Dataset and Benchmark (BSDS300) have been considered and tested against recent multi-level image segmentation methods. The segmentation results are validated in terms of subjective and objective evaluation. Experiments arm that the presented method is efficient and competitive than the existing multi-level image segmentation methods

scholarly journals Brain Tumor Segmentation using Multi Level Thresholding using Fuzzy Entropy

2020 ◽  
Vol 8 (5) ◽  
pp. 2641-2643
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Image Segmentation ◽  
Brain Tumor ◽  
Fuzzy Entropy ◽  
Image Processing Technique ◽  
Tumor Segmentation ◽  
Fuzzy Approach ◽  
Segmentation Process ◽  
Multi Level

In image processing field, image processing technique is used to distinguish the object from its image scene at pixel level. The image segmentation process is the significant task in the processing of image field as well as in image analysis. The most difficult task in the image analysis field is the automatic separation of object from its background. To alleviate this problem several image segmentation process is introduced are gray level thresholding, edge detection method, interactive pixel classification method, neural network approach and segmentation based on fuzzy approach This chapter presents the multilevel set thresholding method using partition of fuzzy approach on brain image histogram and theory of entropy. The fuzzy entropy method is applied on multi-level brain tumor MRI image segmentation method. The threshold of brain MR image is obtained by optimized the entropy measure. In this method, Differential Evolution technique is used to find the best solution.

scholarly journals A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 398 ◽  
Author(s):  
Suhang Song ◽  
Heming Jia ◽  
Jun Ma
Keyword(s):  
Electromagnetic Field ◽  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Fitness Function ◽  
Fuzzy Entropy ◽  
Color Image Segmentation ◽  
Superior Performance ◽  
Computational Time ◽  
Multilevel Thresholding

Multilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above problem, meta-heuristic algorithms have been employed in this field for searching the optima during the past few years. In this paper, an effective technique of Electromagnetic Field Optimization (EFO) algorithm based on a fuzzy entropy criterion is proposed, and in addition, a novel chaotic strategy is embedded into EFO to develop a new algorithm named CEFO. To evaluate the robustness of the proposed algorithm, other competitive algorithms such as Artificial Bee Colony (ABC), Bat Algorithm (BA), Wind Driven Optimization (WDO), and Bird Swarm Algorithm (BSA) are compared using fuzzy entropy as the fitness function. Furthermore, the proposed segmentation method is also compared with the most widely used approaches of Otsu’s variance and Kapur’s entropy to verify its segmentation accuracy and efficiency. Experiments are conducted on ten Berkeley benchmark images and the simulation results are presented in terms of peak signal to noise ratio (PSNR), mean structural similarity (MSSIM), feature similarity (FSIM), and computational time (CPU Time) at different threshold levels of 4, 6, 8, and 10 for each test image. A series of experiments can significantly demonstrate the superior performance of the proposed technique, which can deal with multilevel thresholding color image segmentation excellently.

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