Multilevel Image Thresholding Research Articles

A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding

The multi-level thresholding is a popular method for image segmentation. However, the method is computationally expensive and suffers from premature convergence when level increases. To solve the two problems, this paper presents an advanced version of gravitational search algorithm (GSA), namely hybrid algorithm of GSA with genetic algorithm (GA) (GSA-GA) for multi-level thresholding. In GSA-GA, when premature convergence occurred, the roulette selection and discrete mutation operators of GA are introduced to diversify the population and escape from premature convergence. The introduction of these operators therefore promotes GSA-GA to perform faster and more accurate multi-level image thresholding. In this paper, two common criteria (1) entropy and (2) between-class variance were utilized as fitness functions. Experiments have been performed on six test images using various numbers of thresholds. The experimental results were compared with standard GSA and three state-of-art GSA variants. Comparison results showed that the GSA-GA produced superior or comparative segmentation accuracy in both entropy and between-class variance criteria. Moreover, the statistical significance test demonstrated that GSA-GA significantly reduce the computational complexity for all of the tested images.

Fuzzy Multilevel Image Thresholding Based on Modified Discrete Grey Wolf Optimizer and Local Information Aggregation

Fuzzy entropy and image thresholding are the most direct and effective methods for image segmentation. This paper, taking fuzzy Kapur’s entropy as the optimal objective function, with modified discrete Grey wolf optimizer (GWO) as the tool, uses pseudotrapezoid-shaped to conduct fuzzy membership initialization so as to achieve image segmentation finally by means of local information aggregation. Experiment results show that the proposed fuzzy-based GWO and aggregation algorithm and fuzzy-based modified discrete GWO and aggregation (FMDGWOA) algorithm can search out the optimal thresholds effectively and accurately. In this paper, electro-magnetism optimization based on Kapur’s entropy, standard GWO and fuzzy entropy-based differential evolution algorithm are experimentally compared with the proposed method, respectively. It shows that FMDGWOA enjoys obvious advantages in segmentation quality, objective function, and stability.

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur’s entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image’s spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposed method with Kapur’s entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.

Quantum genetic algorithm for adaptive image multi-thresholding segmentation

An adaptive image multilevel thresholding segmentation algorithm is presented in this paper. The proposed algorithm introduces a parallel quantum genetic algorithm PQGA for histogram-based image segmentation. Quantum genetic algorithm QGA has the advantages of fast convergence speed and strong global search capabilities. And PQGA can improve the computational efficiency of the QGA further. Without predetermining the number of the thresholds, the proposed algorithm that chooses the automatic thresholding criterion as its objective function can obtain the number of the thresholds and the corresponding thresholds accurately. The experimental results demonstrate good performance of the PQGA in solving adaptive multilevel thresholding segmentation problems by comparing with other methods for several test images.

Optimal multi-level thresholding with membrane computing

The conventional methods are not effective and efficient for image multi-level thresholding due to time-consuming and expensive computation cost. The multi-level thresholding problem can be posed as an optimization problem, optimizing some thresholding criterion. In this paper, membrane computing is introduced to propose an efficient and robust multi-level thresholding method, where a cell-like P system with the nested structure of three layers is designed as its computing framework. Moreover, an improved velocity-position model is developed to evolve the objects in membranes based on the special membrane structure and communication mechanism of objects. Under the control of evolution-communication mechanism of objects, the cell-like P system can efficiently exploit the best multi-level thresholds for an image. Simulation experiments on nine standard images compare the proposed multi-level thresholding method with several state-of-the-art multi-level thresholding methods and demonstrate its superiority.

Integration of multiple orientation and texture information for finger-knuckle-print verification

The Competitive Coding (CompCode) scheme, which extracts and codes the local dominant orientation as features, has been widely used in finger knuckle print (FKP) verification. However, CompCode may lose some valuable information such as multiple orientation and texture of the FKP image. To remedy this drawback, a novel multiple orientation and texture information integration scheme is proposed in this paper. As compared with CompCode, the proposed scheme not only considers more orientations, but also introduces a multilevel image thresholding scheme to perform orientation coding on each Gabor filtering response. For texture features extraction, LBP maps are first obtained by performing Local Binary Pattern (LBP) operator on each Gabor filtering response, and then a similar coding scheme is applied on these LBP maps. Finally, multiple orientation and texture features are integrated via score level fusion to further improve FKP verification accuracy. Extensive experiments conducted on the PolyU FKP database show the effectiveness of the proposed scheme.

A Global Multilevel Thresholding Using Differential Evolution Approach

Otsu’s function measures the properness of threshold values in multilevel image thresholding. Optimal threshold values are necessary for some applications and a global search algorithm is required. Differential evolution (DE) is an algorithm that has been used successfully for solving this problem. Because the difficulty of a problem grows exponentially when the number of thresholds increases, the ordinary DE fails when the number of thresholds is greater than 12. An improved DE, using a new mutation strategy, is proposed to overcome this problem. Experiments were conducted on 20 real images and the number of thresholds varied from 2 to 16. Existing global optimization algorithms were compared with the proposed algorithms, that is, DE, rank-DE, artificial bee colony (ABC), particle swarm optimization (PSO), DPSO, and FODPSO. The experimental results show that the proposed algorithm not only achieves a more successful rate but also yields a lower threshold value distortion than its competitors in the search for optimal threshold values, especially when the number of thresholds is large.

Performance assessment of multi-level image thresholding for paper quality inspection

Automatic characterisation and detection of dirt particles in pulp and paper plays a pivotal role in the papermaking industry. Machine vision provides many potential advantages in terms of speed, accuracy and repeatability. Such systems make use of image processing algorithms which aim at separating paper and pulp impurities from the background. The most common approach is based on image thresholding, which consists of determining a set of intensity values that split an image into one or more classes, each representing either the background (i.e., an area with no defects) or an area with some types of contraries. In this paper, we present a quantitative experimental evaluation of four image thresholding methods (i.e., Otsu’s, Kapur’s, Kittler’s and Yen’s) for dirt analysis in paper. The results show that Kittler’s method is the most stable and reliable for this task.

Bio-inspired algorithms for multilevel image thresholding

Bi-level image thresholding methods can be easily extended to multilevel cases. However, extended versions are computationally expensive. In this paper, we propose first a differential evolution DE algorithm using Tsallis entropy as objective function. Second, we conduct a comprehensive comparative study by investigating the potential of the proposed algorithm to find the optimal threshold values along with two other bio-inspired algorithms namely artificial bees colony ABC and particle swarm optimisation PSO. Two entropy-based measures have been considered as objective functions. Real images with different complexities have been used to evaluate the performance of the three algorithms. Experimental results demonstrated that DE and ABC achieve the same quality of solutions in terms of peak signal to noise ratio values and uniformity values. They are more robust than PSO. Furthermore, DE has shown to be the most stable and ABC the fastest with the advantage of employing few control parameters.

An Improved Quantum-Inspired Genetic Algorithm for Image Multilevel Thresholding Segmentation

A multilevel thresholding algorithm for histogram-based image segmentation is presented in this paper. The proposed algorithm introduces an adaptive adjustment strategy of the rotation angle and a cooperative learning strategy into quantum genetic algorithm (called IQGA). An adaptive adjustment strategy of the quantum rotation which is introduced in this study helps improving the convergence speed, search ability, and stability. Cooperative learning enhances the search ability in the high-dimensional solution space by splitting a high-dimensional vector into several one-dimensional vectors. The experimental results demonstrate good performance of the IQGA in solving multilevel thresholding segmentation problem by compared with QGA, GA and PSO.

Multi-level image thresholding by synergetic differential evolution

The multi-level image thresholding is often treated as a problem of optimization. Typically, finding the parameters of these problems leads to a nonlinear optimization problem, for which obtaining the solution is computationally expensive and time-consuming. In this paper a new multi-level image thresholding technique using synergetic differential evolution (SDE), an advanced version of differential evolution (DE), is proposed. SDE is a fusion of three algorithmic concepts proposed in modified versions of DE. It utilizes two criteria (1) entropy and (2) approximation of normalized histogram of an image by a mixture of Gaussian distribution to find the optimal thresholds. The experimental results show that SDE can make optimal thresholding applicable in case of multi-level thresholding and the performance is better than some other multi-level thresholding methods.

Multilevel Image Thresholding with Glowworm Swam Optimization Algorithm based on the Minimum Cross Entropy

The measure of cross entropy is a widely used index applied to many areas of computer science. In this article, we proposed a new multilevel thresholding algorithm for image segmentation using the glowworm swarm optimization based on the criterion of minimum cross entropy (MCE). In this paper, a new multilevel MCE thresholding (MCET) algorithm using the glowworm swam optimization (GSO) algorithm is proposed. The proposed image thresholding algorithm is called the GSO-based MCET algorithm. The five different methods including the exhaustive search, the honey bee mating optimization (HBMO), the firefly (FF) algorithm, the artificial bee colony algorithm (ABC) and the particle swarm optimization (PSO) are also implemented for performance comparison. The experimental results demonstrate that the proposed GSO-based MCET algorithm can efficiently search for multiple thresholds that are very close to the optimal ones examined by the exhaustive search method. Compared with the other five thresholding methods, the needs of computation time using the FF-based MCET algorithm is the smallest. Furthermore, the segmentation performance of GSO-based MCET algorithm is better than the PSO-based MCET algorithm, while the results of GSO-based MCET algorithm are not significant different to the other three bio-inspired computing algorithms.

Optimization of Multilevel Image Thresholding Using the Bees Algorithm

Optimization of Multilevel Image Thresholding Using the Bees Algorithm

A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding

Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu’s method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various meta-heuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an onlooker bee to select an employee bee was modified to serve our purposes. The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance. The ranking was performed by Friedman ranks. The experimental results showed that IABC/best/1 outperformed the other techniques when all of them were applied to multilevel image thresholding. Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm.

A multilevel image thresholding using the honey bee mating optimization

Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the maximum entropy thresholding (MET) has been widely applied. In this paper, a new multilevel MET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. This proposed method is called the maximum entropy based honey bee mating optimization thresholding (MEHBMOT) method. Three different methods such as the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO) and the Fast Otsu’s method are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed MEHBMOT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other three thresholding methods, the segmentation results using the MEHBMOT algorithm is the best and its computation time is relatively low. Furthermore, the convergence of the MEHBMOT algorithm can rapidly achieve and the results validate that the proposed MEHBMOT algorithm is efficient.

On improvement of the computation speed of Otsu’s image thresholding

Many previous methods for image thresholding focused on developing automatic algorithms to determine thresholds. However, most of the methods suffer from time-consuming computation for multilevel thresholding. Therefore, a fast and automatic thresholding method is desired for real-time applications. This paper proposes a new and faster method for bilevel as well as multilevel image thresholding. Taking (partial) derivatives of image between-class variance with respect to gray levels develops the proposed method. For bilevel thresholding, a nonlinear equation is derived to solve for an optimal threshold. For multilevel thresholding, a set of nonlinear equations is derived to solve for a set of optimal thresholds. A parameter is introduced to determine the class number for image classification by subjective determination of the ratio of image features to be kept after classification. Statistical performance analysis of the proposed method versus the Baysian classifier is included in this paper. Thresholding computation for the proposed method and Otsu's [N. Otsu, "A threshold selection method from gray-level histograms," IEEE Trans. Syst. Man, Cyber. SMC-9, 62–66 (1979)] is discussed. There are also several examples to illustrate the feasibility of the proposed method and its superiority in computation speed.

A Hybrid Approach Using Gaussian Smoothing and Genetic Algorithm for Multilevel Thresholding

In this paper, a hybrid approach, which is based on Gaussian smoothing and a genetic algorithm (GA), is proposed for automatic multilevel image thresholding. Using a mixture probability density function of several Gaussian functions to fit an image histogram and then find the optimal threshold(s) is a well-known optimal thresholding method. In the proposed approach, the Gaussian kernel smoothing is used to estimate the number of classes in an image. Since the parameter estimation in the method is typically a nonlinear optimization problem, the parameters used in the mixture of Gaussian functions that give the best fit to the processed histogram are determined using GA. In experiments, synthetic data and real images were processed to evaluate the thresholding performance. The experimental results to confirm the proposed approach are also included.

Parallel entropic auto-thresholding

In this paper, we examine a multi-level thresholding algorithm based on a number of phases including peak-search, fuzzy logic and entropy of the fuzzy membership function. Analysis of the algorithm is presented to show its properties and behaviours at the various cascaded stages. The fuzzy entropy function of the image histogram is computed using S-function membership and Shannon's entropy function. To establish a suitable fuzzy region bandwidth, we have used a peak-search method based on successive clipping of the image histogram. Location of the valleys in the entropy function correspond to the certainties within the fuzzy region of the image. These certainties are used to indicate an optimal segmentation pattern for multi-level image thresholding. We compare and contrast this method of thresholding with a maximum entropy method. We have implemented the technique in parallel on a transputer-based machine as well as on a cluster of SUN4 workstations, availing ourselves of the PVM communication kernel. A parallel algorithm for the maximum entropy method is given, which significantly reduces computation times. An objective method is used to evaluate the resulting images.