Multilevel Thresholding Image Segmentation Research Articles

Image Segmentation of Brain MR Images Using Otsu’s based Hybrid WCMFO Algorithm

In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T₂-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate. In contrast to other state-of-the-art methods, namely Adaptive Wind Driven Optimization (AWDO), Adaptive Bacterial Foraging (ABF) and Particle Swarm Optimization (PSO), the proposed algorithm has been found to be better at producing the best objective function, Peak Signal-to-Noise Ratio (PSNR), Standard Deviation (STD) and lower computational time values. Further, it was observed thatthe segmented image gives greater detail when the threshold level increases. Moreover, the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus, these images will lead to better segments of gray, white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.

Improved Artificial Bee Colony Using Sine-Cosine Algorithm for Multi-Level Thresholding Image Segmentation

Multilevel-thresholding is an efficient method used in image segmentation. This paper presents a hybrid meta-heuristic approach for multi-level thresholding image segmentation by integrating both the artificial bee colony (ABC) algorithm and the sine-cosine algorithm (SCA). The proposed algorithm, called ABCSCA, is applied to segment images and it utilizes Otsu's function as the objective function. The proposed ABCSCA uses ABC to optimize the threshold and to reduce the search region. Thereafter, the SCA algorithm uses the output of ABC to determine the global optimal solution, which represents the thresholding values. To evaluate the performance of the proposed ABCSCA, a set of experimental series is performed using nineteen images. In the first experimental series, the proposed ABCSCA is assessed at the low threshold levels and compared with the ABC and SCA as traditional methods. Moreover, the second experimental series aims to evaluate the ABCSCA at high threshold levels and it is compared with six algorithms in addition to the SCA and ABC. Besides, the proposed method is evaluated using the fuzzy entropy. The results demonstrate the effectiveness of the proposed algorithm and showed that it outperforms other algorithms in terms of performance measures, such as Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index (SSIM).

A comparative study of nature inspired optimization algorithms on multilevel thresholding image segmentation

In this paper, five successful nature inspired algorithms; the artificial tree algorithm (AT), the particle swarm optimization (PSO), the genetic algorithm (GA), the cultural algorithm (CA), and the cuckoo search algorithm (CS) have been compared on multilevel image thresholding. The segmentation process is based on the Levine and Nazif intra class uniformity criterion which is seen as an optimization problem. The comparison performances are in terms of the value of the objectif function, the peak signal to noise ratio (PSNR) and the computation time. Empirical results over different benchmark images for different threshold numbers reveal the robustness, the reliability and the rapidity of the cultural algorithm (CA).

2-D histogram Based Multilevel Thresholding for Image Segmentation by Hybrid Bacterial Foraging Optimization and Particle Swarm Optimization

The objective of image segmentation is to extract meaningful clusters in given image. Meaningful clusters are possible with perfect threshold values which are optimized by assuming Renyi entropy as an objective function. A 1-D histogram based multilevel thresholding is computationally complex and segmented image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. To overcome the problem, a 2-D histogram based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel Hybrid Bacterial Foraging Optimization Algorithm and Particle Swarm Optimization (hBFOA-PSO) and the obtained results are compared with state of art optimization techniques. The results of the proposed model have been evaluated on a standard image dataset. The results obtained after implementing a 2-D histogram suggest hBFOA-PSO can be effectively used won multilevel thresholding problems resulting in a high accuracy.

Masi entropy based multilevel thresholding for image segmentation

A new multilevel thresholding based image segmentation technique is developed which utilizes Masi entropy as an objective function. Thresholding is an important image segmentation technique. It may be divided into two types such as bi-level and multilevel thresholding. Bi-level thresholding uses a single threshold to classify an image into two classes: object and the background. For an image containing a single object in a distinct background, bi-level thresholding can be successfully used for segmentation. But in case of complex images containing multiple objects, bi-level thresholding often fails to give satisfactory segmentation. In such cases, multilevel thresholding is generally preferred over bi-level thresholding. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are generally used to optimize the threshold searching process to reduce the computational complexity involved in multilevel thresholding. In this paper, Particle Swarm Optimization (PSO) along with Masi entropy is proposed for multilevel thresholding based image segmentation. The proposed technique is evaluated using a set of standard test images. The proposed technique is compared with the recently proposed Dragonfly Algorithm (DA) based technique that uses Kapur’s entropy as objective function. The proposed technique is also compared with PSO based technique that uses minimum cross entropy (MCE) as objective function. The quality of the segmented images is measured using Mean Structural SIMilarity (MSSIM) index and Peak Signal-to-Noise Ratio (PSNR). The experimental results suggest that the proposed technique outperforms Kapur’s entropy and gives very competitive result when compared with the MCE based technique. Further, computational complexity of multilevel thresholding is also greatly reduced.

Swarm selection method for multilevel thresholding image segmentation

Multilevel thresholding is one of the most popular approaches used for image segmentation. Several methods have been used to find the threshold values; however, metaheuristic (MH) methods achieve better results than the other classical methods. Nevertheless, each metaheuristic method has its limitation such as high computation; moreover, it can suffer from premature convergence when the level of the threshold increases. To solve this problem, there is a trend to hybridize the MH algorithms together; however, there are several MH algorithms that can be combined. Consequently, we need to seriously determine which combination and number of such algorithms must be combined. Therefore, this paper proposes an intelligent framework that provides experts with a tool for solving the problem through selecting a suitable number of swarm algorithms from eleven such algorithms. The proposed method, which is called swarm selection (SS), distributes the eleven algorithms into two groups, the first of which is called the control group and only contains the differential evolution (DE) algorithm. The aim of the DE algorithm in the control group is to determine the best combination of the other ten algorithms in the second group. Meanwhile, the selected algorithms in the second group work together to find the optimal threshold values that maximize the Otsu function. A series of experiments have been performed on six test images using nine threshold levels. The performance of the proposed SS method is compared with the other three hybrid algorithms and two non-hybrid algorithms. The experimental results demonstrate that the proposed SS method outperforms the other image segmentation methods in terms of the performance measures, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), fitness function value and computational time. In addition, by using this intelligent framework, experts performing applications that depend on image segmentation will save computational time in identifying suitable methods.

Fuzzy Multi-level Color Satellite Image Segmentation Using Nature-Inspired Optimizers: A Comparative Study

In the realm of image processing domain, segmentation is an indispensable method for various applications. One can segment an image according to shape, size, regularities, structure, color, etc. Multi-level thresholding for image segmentation is one of the most promising methods for segmentation in the recent era. However, multi-level thresholding is computationally expensive, tedious and also challenging because of finding the optimal threshold values. Thus, to address this issue, this study presents a stochastic fractal search (SFS) with fuzzy entropy-based multi-level thresholding model for the proper segmentation of color satellite images. To prove the superiority of SFS algorithm, a comparative study is performed with four well-known nature-inspired optimization algorithms, namely particle swarm optimization (PSO), cuckoo search (CS), harmony search (HS) and artificial bee colony (ABC) algorithms. The experiment has been conducted on various satellite images, and the result shows that SFS with fuzzy entropy-based model provides superior-quality segmented images over other methods in terms of fitness value, computational time and values of quality metrics. The experimental study also shows that computational time of SFS algorithms is 2.5% less than CS algorithms and 8%, 9%, 15% less than ABC, PSO, HS, respectively, on average when the same number function evaluations has been considered as stopping criterion.

A non-revisiting quantum-behaved particle swarm optimization based multilevel thresholding for image segmentation

Multilevel thresholding for image segmentation is one of the crucial techniques in image processing. Even though numerous methods have been proposed in literature, it is still a challenge for the existing methods to produce steady satisfactory thresholds at manageable computational cost in segmenting images with various unknown properties. In this paper, a non-revisiting quantum-behaved particle swarm optimization (NrQPSO) algorithm is proposed to find the optimal multilevel thresholds for gray-level images. The proposed NrQPSO uses the non-revisiting scheme to avoid the re-evaluation of the evaluated solution candidates. To reduce the unnecessary computation cost, the NrQPSO provides an automatic stopping mechanism which is capable of gauging the progress of exploration and stops the algorithm rationally in a natural manner. For further improving the computation efficiency, the NrQPSO employs a meticulous solution search method to overcome the drawback of the existing QPSO algorithms using the original search methods. Performance of the NrQPSO is tested on the Berkeley segmentation data set. The experimental results have demonstrated that the NrQPSO can outperform the other state-of-the-art population-based thresholding methods in terms of efficiency, effectiveness and robustness; thus, the NrQPSO can be applied in real-time massive image processing.

Human mental search-based multilevel thresholding for image segmentation

Multilevel thresholding is one of the principal methods of image segmentation. These methods enjoy image histogram for segmentation. The quality of segmentation depends on the value of the selected thresholds. Since an exhaustive search is made for finding the optimum value of the objective function, the conventional methods of multilevel thresholding are time-consuming computationally, especially when the number of thresholds increases. Use of evolutionary algorithms has attracted a lot of attention under such circumstances. Human mental search algorithm is a population-based evolutionary algorithm inspired by the manner of human mental search in online auctions. This algorithm has three interesting operators: (1) clustering for finding the promising areas, (2) mental search for exploring the surrounding of every solution using Levy distribution, and (3) moving the solutions toward the promising area. In the present study, multilevel thresholding is proposed for image segmentation using human mental search algorithm. Kapur (entropy) and Otsu (between-class variance) criteria were used for this purpose. The advantages of the proposed method are described using twelve images and in comparison with other existing approaches, including genetic algorithm, particle swarm optimization, differential evolution, firefly algorithm, bat algorithm, gravitational search algorithm, and teaching-learning-based optimization. The obtained results indicated that the proposed method is highly efficient in multilevel image thresholding in terms of objective function value, peak signal to noise, structural similarity index, feature similarity index, and the curse of dimensionality. In addition, two nonparametric statistical tests verified the efficiency of the proposed algorithm, statistically.

Many-objectives multilevel thresholding image segmentation using Knee Evolutionary Algorithm

Image segmentation is one of the popular tasks in image processing that can be used in several applications. For that, there are several methods have been proposed for image segmentation; in which, these methods aim to minimize or maximize single objective (SO) function to find the optimal threshold to separate the image into the optimal number of regions. While a few of the image segmentation methods consider multi-objective functions which aim to find the optimal solutions that can reduce the conflict among the different objectives. However, these methods have some limitations as decreasing their performance when the number of objectives is increasing. Due to increasing the number of non-dominated solutions, so, there isn’t pressure towards the Pareto front. Therefore, this paper proposes an alternative image segmentation method using many-objective optimization (MaOP) algorithms considering seven objective functions. One of the most competitive MaOPs is called the Knee Evolutionary Algorithm (KnEA) which used to find the set of Pareto optimal solutions for seven objective functions to improve the image segmentation. The proposed KnEA is evaluated using a set of six images tested at six different levels of threshold, and its performance is compared with other MaOP methods. The experimental results show that the KnEA method has a better approximation to the optimal Pareto fronts (PFs) than the other MaOPs method in terms of the quality of the segmented image such as the peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), and the computational time. As well as, the quality of PFs is measuring using the hypervolume, coverage and spacing indicators.

A generalized Masi entropy based efficient multilevel thresholding method for color image segmentation

Multilevel thresholding for image segmentation is a crucial process in several applications such as feature extraction and pattern recognition. In this paper, a novel Masi entropy-based criterion f...

Multilevel Thresholding for Image Segmentation Using an Improved Electromagnetism Optimization Algorithm

Image segmentation is considered one of the most important tasks in image processing, which has several applications in different areas such as; industry agriculture, medicine, etc. In this paper, we develop the electromagnetic optimization (EMO) algorithm based on levy function, EMO-levy, to enhance the EMO performance for determining the optimal multi-level thresholding of image segmentation. In general, EMO simulates the mechanism of attraction and repulsion between charges to develop the individuals of a population. EMO takes random samples from search space within the histogram of image, where, each sample represents each particle in EMO. The quality of each particle is assessed based on Otsu’s or Kapur objective function value. The solutions are updated using EMO operators until determine the optimal objective functions. Finally, this approach produces segmented images with optimal values for the threshold and a few number of iterations. The proposed technique is validated using different standard test images. Experimental results prove the effectiveness and superiority of the proposed algorithm for image segmentation compared with well-known optimization methods

A Multilevel Image Thresholding Based on Hybrid Salp Swarm Algorithm and Fuzzy Entropy

The image segmentation techniques based on multi-level threshold value received lot of attention in recent years. It is because they can be used as a pre-processing step in complex image processing applications. The main problem in identifying the suitable threshold values occurs when classical image segmentation methods are employed. The swarm intelligence (SI) technique is used to improve multi-level threshold image (MTI) segmentation performance. SI technique simulates the social behaviors of swarm ecosystem, such as the behavior exhibited by different birds, animals etc. Based on SI techniques, we developed an alternative MTI segmentation method by using a modified version of the salp swarm algorithm (SSA). The modified algorithm improves the performance of various operators of the moth-flame optimization (MFO) algorithm to address the limitations of traditional SSA algorithm. This results in improved performance of SSA algorithm. In addition, the fuzzy entropy is used as objective function to determine the quality of the solutions. To evaluate the performance of the proposed methodology, we evaluated our techniques on CEC2005 benchmark and Berkeley dataset. Our evaluation results demonstrate that SSAMFO outperforms traditional SSA and MFO algorithms, in terms of PSNR, SSIM and fitness value.

A novel artificial bee colony optimiser with dynamic population size for multi-level threshold image segmentation

Existing swarm intelligence (SI) models are usually derived from fixed-population biological system. However, this approach inevitably causes unnecessary computational cost. In addition, the population size of these models is usually hard to be pr-determined appropriately. In this contribution, this paper exploits a general varying-population swarm model (VPSM) with life-cycle foraging rules based on the population growth dynamic principle. This model essentially improves individual-level adaptability and population-level emergence to self-adapt towards an optimal population size. Then, a novel VPSM-based artificial bee colony optimiser is instantiated with orthogonal Latin squares approach and crossover-based social learning strategies. A comprehensive experimental analysis is implemented in which the proposed algorithm is benchmarked against classical bio-mimetic algorithms on CEC2014 test suites. Then, this algorithm is applied for multi-level image segmentation. Computation results show the performance superiority of the proposed algorithm.

A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation

Multilevel thresholding for image segmentation is a crucial process in several applications such as feature extraction and pattern recognition. The meticulous search for the best values for the optimization of fitness function using classical operations needs profuse computational time, which also results in inaccuracy and instability. In this paper, a new beta differential evolution (BDE)-based fast color image multilevel thresholding scheme using two objective functions has been presented. The optimal threshold values are determined by maximizing Kapur’s and Tsallis entropy (entropy criterion) thresholding functions coupled with BDE algorithm. The efficiency of the proposed method is examined over existing multilevel thresholding methods such as artificial bee colony, particle swarm optimization, wind-driven optimization and differential evolution. These approaches are aimed to determine optimum threshold values at different levels of thresholding for color image segmentation. The proficiency of the presented methodology is demonstrated visually and computationally on five real-life true color images as well as four satellite images. Experimental outcomes are exhibited in terms of the optimal threshold value, best objective function and computational cost (in seconds) for each method at different thresholding levels. Afterward, the proposed scheme is examined intensively regarding the superiority of quality. The experimentally evaluated results show that the proposed BDE-based approach for multilevel color image segmentation can accurately and efficiently examine for multiple thresholds, which are near to optimal ones searched using an exhaustive search process.

Multilevel Thresholding for Image Segmentation Based on Cellular Metaheuristics

This article describes how multilevel thresholding image segmentation is a process used to partition an image into well separated regions. It has various applications such as object recognition, edge detection, and particle counting, etc. However, it is computationally expensive and time consuming. To alleviate these limitations, nature inspired metaheuristics are widely used to reduce the computational complexity of such problem. In this article, three cellular metaheuristics namely cellular genetic algorithm (CGA), cellular particle swarm optimization (CPSO) and cellular differential evolution (CDE) are adapted to solve the multilevel thresholding image segmentation problem. Experiments are conducted on different test images to assess the performance of the cellular algorithms in terms of efficiency, quality and stability based on the between-class variance and Kapur's entropy as objective functions. The experimental results have shown that the proposed cellular algorithms compete with and even outperform existing methods for multilevel thresholding image segmentation.

Multilevel thresholding for image segmentation using Krill Herd Optimization algorithm

In this paper a novel multilevel thresholding algorithm using a meta-heuristic Krill Herd Optimization (KHO) algorithm has been proposed for solving the image segmentation problem. The optimum threshold values are determined by the maximization of Kapur’s or Otsu’s objective function using Krill Herd Optimization technique. The proposed method reduces the computational time for computing the optimum thresholds for multilevel thresholding. The applicability and computational efficiency of the Krill Herd Optimization based multilevel thresholding is demonstrated using various benchmark images. A detailed comparative analysis with other existing bio-inspired techniques based multilevel thresholding techniques such as Bacterial Foraging (BF), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Moth-Flame Optimization (MFO) has been performed to prove the superior performance of the proposed method.

Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation

Multilevel thresholding is a very important image processing technique in the field of image segmentation. However, the computational complexity of determining the optimal threshold grows exponentially with increasing thresholds. To overcome this drawback, in this paper, we propose a multi-threshold image segmentation method based on the moth swarm algorithm. The meta-heuristic algorithm uses Kapur’s entropy method to optimize the thresholds for eight standard test images. When compared with other state-of-the-art evolutionary algorithms, the proposed method proved to be robust and effective according to numerical experimental results and image segmentation results. This indicates the high performance of the method for the segmentation of digital images.

An enhanced artificial bee colony optimizer and its application to multi-level threshold image segmentation

A modified artificial bee colony optimizer (MABC) is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main idea of MABC is to enrich artificial bee foraging behaviors by combining local search and comprehensive learning using multi-dimensional PSO-based equation. With comprehensive learning, the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the local search enables the bees deeply exploit around the promising area, which provides a proper balance between exploration and exploitation. The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the MABC algorithm to image segmentation problem. Experimental results verify the effectiveness of the proposed algorithm.

A multi-level thresholding image segmentation based on an improved artificial bee colony algorithm

As a popular evolutionary algorithm, Artificial Bee Colony (ABC) algorithm has been successfully applied into threshold-based image segmentation. Due to its one dimension search strategy, the convergence speed of ABC is slow and its solution is acceptable but not precise. For making more fine-tuning search and further enhancing the achievements on image segmentation, we proposed an Otsu segmentation method based on a new ABC algorithm. Different from the traditional ABC strategy, our algorithm takes full use of individuals information which is defined by a focus point and the best point to increase its accuracy and convergence speed. Furtheremore, we propose an adaptive parameter to adjust the search step of individual automatically, which also improves its exploitation ability. Experimental results on Berkeley segmentation database demonstrate the effectiveness of our algorithm.