Multilevel Thresholding Method Research Articles

Multilevel thresholding using grey wolf optimizer for image segmentation

Abstract Multilevel thresholding is one of the most important areas in the field of image segmentation. However, the computational complexity of multilevel thresholding increases exponentially with the increasing number of thresholds. To overcome this drawback, a new approach of multilevel thresholding based on Grey Wolf Optimizer (GWO) is proposed in this paper. GWO is inspired from the social and hunting behaviour of the grey wolves. This metaheuristic algorithm is applied to multilevel thresholding problem using Kapur's entropy and Otsu's between class variance functions. The proposed method is tested on a set of standard test images. The performances of the proposed method are then compared with improved versions of PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based multilevel thresholding methods. The quality of the segmented images is computed using Mean Structural SIMilarity (MSSIM) index. Experimental results suggest that the proposed method is more stable and yields solutions of higher quality than PSO and BFO based methods. Moreover, the proposed method is found to be faster than BFO but slower than the PSO based method.

Multi-objective and multi-level image thresholding based on dominance and diversity criteria

The Kapur and Otsu methods are widely used image thresholding approaches and they are very efficient in bi-level thresholding applications. Evolutionary algorithms have been developed to extend the Kapur and Otsu methods to the multi-level thresholding case. However, there remains an unsolved argument that neither Kapur nor Otsu objective can optimally fit diverse content contained in different kinds of images. This paper proposes a multi-objective model which seeks to find the Pareto-optimal set with respect to Kapur and Otsu objectives. Based on dominance and diversity criteria, we developed a hybrid multi-objective particle swarm optimization (MOPSO) method by incorporating several intelligent search strategies. The ensemble strategy is also applied to automatically select the best search strategy to perform at various algorithm stages according to its historic performances. The experimental result shows that the solutions to our multi-objective model consistently produce equal or better segmentation results than those by the optimal solutions to the original Kapur and Otsu models, and that the proposed hybrid algorithm with and without the ensemble strategy produces a better approximation to the ideal Pareto front than those obtained by two other MOPSO variants and the MOEA/D. In comparison with the most recent multilevel thresholding methods, our approach also consistently obtains better performance in the segmentation result for several benchmark images.

Multilevel Thresholding Method Based on Electromagnetism for Accurate Brain MRI Segmentation to Detect White Matter, Gray Matter, and CSF.

This work explains an advanced and accurate brain MRI segmentation method. MR brain image segmentation is to know the anatomical structure, to identify the abnormalities, and to detect various tissues which help in treatment planning prior to radiation therapy. This proposed technique is a Multilevel Thresholding (MT) method based on the phenomenon of Electromagnetism and it segments the image into three tissues such as White Matter (WM), Gray Matter (GM), and CSF. The approach incorporates skull stripping and filtering using anisotropic diffusion filter in the preprocessing stage. This thresholding method uses the force of attraction-repulsion between the charged particles to increase the population. It is the combination of Electromagnetism-Like optimization algorithm with the Otsu and Kapur objective functions. The results obtained by using the proposed method are compared with the ground-truth images and have given best values for the measures sensitivity, specificity, and segmentation accuracy. The results using 10 MR brain images proved that the proposed method has accurately segmented the three brain tissues compared to the existing segmentation methods such as K-means, fuzzy C-means, OTSU MT, Particle Swarm Optimization (PSO), Bacterial Foraging Algorithm (BFA), Genetic Algorithm (GA), and Fuzzy Local Gaussian Mixture Model (FLGMM).

Segmentation fusion based on neighboring information for MR brain images

In this paper, we study on how to boost image segmentation algorithms. First of all, a novel fusion scheme is proposed to combine different segmentations with mutual information to reduce misclassified pixels and obtain an accurate segmentation. As the class label of each pixel depends on the pixel's gray level and neighbors' labels, the fusion scheme takes both spatial and intensity information of pixels into account. Then, a detail thresholding segmentation case is designed using the proposed fusion scheme. In the case, the local Laplacian filter is used to get the smoothed version of original image. To accelerate segmentation, a discrete curve evolution based Otsu method is employed to segment the original image and its smoothed version to get two different segmentation maps. The fusion scheme is used to fuse the two maps to get the final segmentation result. Experiments on medical MR-T2 brain images are conducted to demonstrate the effectiveness of the proposed segmentation fusion method. The experimental results indicate that the proposed algorithm can improve segmentation accuracy and it is superior to other multilevel thresholding methods.

A two-dimensional multilevel thresholding method for image segmentation

In this work, we develop a two-dimensional multilevel thresholding technique based on Rényi and Tsallis entropies. The formulation of the proposed method gives rise to an NP-hard combinatorial optimization problem. In order to solve efficiently this problem, two leading evolutionary algorithms, namely the quantum genetic algorithm (QGA) and the differential evolution (DE) have been employed and compared. The effectiveness of both the proposed method and the optimizers was demonstrated on a sample of real-world and synthetic images showing different types of gray-level distributions. Moreover, the contribution of the two-dimensional histogram to the segmentation quality has been highlighted on some images corrupted by noise and containing shadow or reflection effects. Experimental results demonstrated, first, that DE is less time consuming than QGA which is slightly more efficient on complex problems. Second, the Rényi and Tsallis entropies leads to similar image segmentation quality. Finally, we have shown that the proposed method is more appropriate than bilevel thresholding for multimodal and noisy images segmentation.

A honey-bee-mating based algorithm for multilevel image segmentation using Bayesian theorem

The image thresholding techniques are considered as a must for objects segmentation, compression and target recognition, and they have been widely studied for the last few decades; for example, the multi-level thresholding methods, and as such (they) render more great challenges for image segmentation techniques that remain computationally more expensive, when their choices of threshold numbers were increased. Therefore, our aim was to propose an algorithm based on Bayesian theorem and the so-called honey-bee-mating algorithm (HBMA), called a Bayesian honey bee mating algorithm BHBMA. It can not only reduce the computational time and curse of dimensionality, but also can run more reliably and more stably. This enhanced capability was technically accomplished by embedding a new population initialization strategy based on the characteristics of multi-level thresholding technique in pixel-based intensity images arranged from lower grey levels to higher ones. Extensive experiments have shown that our proposed method outperformed other state-of-the-art algorithms empirically in terms of their effectiveness and efficiency, when applying to complex image processing scenario such as automatic target recognition.

Improved Glowworm Swarm Optimization Algorithm for Multilevel Color Image Thresholding Problem

The thresholding process finds the proper threshold values by optimizing a criterion, which can be considered as a constrained optimization problem. The computation time of traditional thresholding techniques will increase dramatically for multilevel thresholding. To greatly overcome this problem, swarm intelligence algorithm is widely used to search optimal thresholds. In this paper, an improved glowworm swarm optimization (IGSO) algorithm has been presented to find the optimal multilevel thresholds of color image based on the between-class variance and minimum cross entropy (MCE). The proposed methods are examined on standard set of color test images by using various numbers of threshold values. The results are then compared with those of basic glowworm swarm optimization, adaptive particle swarm optimization (APSO), and self-adaptive differential evolution (SaDE). The simulation results show that the proposed method can find the optimal thresholds accurately and efficiently and is an effective multilevel thresholding method for color image segmentation.

A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation.

Multi-threshold image segmentation is a powerful image processing technique that is used for the preprocessing of pattern recognition and computer vision. However, traditional multilevel thresholding methods are computationally expensive because they involve exhaustively searching the optimal thresholds to optimize the objective functions. To overcome this drawback, this paper proposes a flower pollination algorithm with a randomized location modification. The proposed algorithm is used to find optimal threshold values for maximizing Otsu's objective functions with regard to eight medical grayscale images. When benchmarked against other state-of-the-art evolutionary algorithms, the new algorithm proves itself to be robust and effective through numerical experimental results including Otsu's objective values and standard deviations.

Real-time automatic multilevel color video thresholding using a novel class-variance criterion

Color image segmentation is a crucial preliminary task in robotic vision systems. This paper presents a novel automatic multilevel color thresholding algorithm to address this task efficiently. The proposed algorithm consists of a learning process and a multi-threshold searching process. The learning process learns the color distribution of an input video sequence in HSV color space, and the multi-threshold searching process automatically determines the optimal multiple thresholds to segment all colors-of-interest in the video based on a novel class-variance criterion. For the learning process, a simple and efficient color-distribution learning algorithm operating with a color-pixel extraction method is proposed to learn a color distribution model of all colors-of-interest in the video images, which simplifies the search for optimal thresholds for the colors-of-interest through a conventional multilevel thresholding method. For the multi-threshold searching process, a nonparametric multilevel color thresholding algorithm with an extended within-class variance criterion is proposed to automatically find the optimal upper bound and lower bound threshold values of each color channel. Experimental results validate the performance and computational efficiency of the proposed method by comparing with three existing methods, both visually and quantitatively.

A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution

We propose a novel multi-level thresholding method for unsupervised separation between objects and background from a natural color image using the concept of the minimum cross entropy (MCE). MCE based thresholding techniques are widely popular for segmenting grayscale images. Color image segmentation is still a challenging field as it involves 3-D histogram unlike the 1-D histogram of grayscale images. Effectiveness of entropy based multi-level thresholding for color image is yet to be explored and this paper presents a humble contribution in this context. We have used differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The proposed method is evaluated by comparing it with seven other prominent algorithms both qualitatively and quantitatively using a well known benchmark suite – the Barkley Segmentation Dataset (BSDS300) with 300 distinct images. Such comparison reflects the efficiency of our algorithm

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.

Optimal multilevel thresholding using molecular kinetic theory optimization algorithm

Multilevel thresholding is one of the most widely used techniques for image segmentation. However, the conventional multilevel thresholding methods are time-consuming algorithms since they exhaustively search for the optimal thresholds to optimize the objective functions. In this paper, a molecular kinetic theory optimization algorithm (MKTOA) is applied to overcome this drawback. MKTOA is used to find the optimal threshold values for maximizing the Kapur’s and Otsu’s objective functions. Three different methods are compared to this proposed method: the molecular force based particle swarm optimization (MPSO) algorithm, the differential evolution (DE) algorithm and the bacterial foraging (BF) algorithm. Experimental results show that MKTOA is much better in terms of robustness, computational efficiency, peak signal to noise ratio (PSNR) and ability to conquer “the Curse of Dimensionality” than MPSO, DE and BF.

Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.

A Multilevel Thresholding algorithm using electromagnetism optimization

Segmentation is one of the most important tasks in image processing. It consists of classifying the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for Multilevel Thresholding is computationally expensive since they exhaustively search the best values to optimize the objective function. Under such conditions, the use of optimization evolutionary approaches has been extended. The Electromagnetism-like Algorithm (EMO) is an evolutionary method which mimics the attraction–repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. In this paper, a Multilevel Thresholding (MT) algorithm based on the EMO is introduced. The approach combines the good search capabilities of EMO algorithm with objective functions proposed by the popular MT methods of Otsu and Kapur. The algorithm takes random samples from a feasible search space inside the image histogram. Such samples build each particle in the EMO context whereas its quality is evaluated considering the objective that is function employed by the Otsu׳s or Kapur׳s method. Guided by these objective values the set of candidate solutions are evolved through the EMO operators until an optimal solution is found. The approach generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image in a reduced number of iterations. Experimental results show performance evidence of the implementation of EMO for digital image segmentation.

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.

Segmentation-based view synthesis for multi-view video plus depth

In this paper, we present a novel view synthesis algorithm for three-dimensional video. The proposed algorithm is based on segmentation using multi-level thresholding method. Recently, numerous techniques have been suggested which use a 2-D color image and the per-pixel depth map of the scene to create virtual views of the scene from any viewing position. However, inaccuracy in the depth maps cause annoying visual artifacts in depth-based view synthesis. In the proposed method, the depth maps are first preprocessed to avoid the errors caused by wrong depth values. Then, the color images are segmented according to the depth values and the regions belonging to different segments are warped independently. To further enhance the quality of the synthesized views, a multi-level thresholding based ghost removal algorithm and a novel hole filling algorithm have been proposed. Experimental results show that the proposed methods achieve an average PSNR gain of 0.98 dB for the multi-view test sequences and also improve the subjective quality of the synthesized views.

Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy

The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur’s entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur’s entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur’s entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.

OPTIMUM MULTILEVEL THRESHOLDING HYBRID GA-PSO BY ALGORITHM

The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, these methods are computationally very expensive for use in multilevel thresholding because the search of optimum threshold do in depth to optimize the objective function. To overcome these drawbacks, a hybrid method of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called GA-PSO, based multilevel thresholding is presented in this paper. GA-PSO algorithm is used to find the optimal threshold value to maximize the objective function of the Otsu method. GA-PSO method proposed has been tested on five standard test images and compared with particle swarm optimization algorithm (PSO) and genetic algorithm (GA). The results showed the effectiveness in the search for optimal multilevel threshold of the proposed algorithm.

Multi-Level Thresholding based on Non-Parametric Approaches for Fast Segmentation

Purpose: In image segmentation via thresholding, Otsu and Kapur methods have been widely used because of their effectiveness and robustness. However, computational complexity of these methods grows exponentially as the number of thresholds increases due to the exhaustive search characteristics. Methods: Particle swarm optimization (PSO) and genetic algorithms (GAs) can accelerate the computation. Both methods, however, also have some drawbacks including slow convergence and ease of being trapped in a local optimum instead of a global optimum. To overcome these difficulties, we proposed two new multi-level thresholding methods based on Bacteria Foraging PSO (BFPSO) and real-coded GA algorithms for fast segmentation. Results: The results from BFPSO and real-coded GA methods were compared with each other and also compared with the results obtained from the Otsu and Kapur methods. Conclusions: The proposed methods were computationally efficient and showed the excellent accuracy and stability. Results of the proposed methods were demonstrated using four real images.

A Location Method for Multi-style License Plates Based on Character Classification

Abstract To locate the license plate exactly is a challenging task, especially for the license plates with complex backgrounds or shadows. Firstly, we use a multilevel local adaptive thresholding method instead of global thresholding methods to produce binary images under varying illuminations. Secondly, the connected contour analysis is applied to extract the main characters, and the false character contour regions are filtered out by the SVM classification method using PCA dimensionality reduction. Finally, we merge the remaining characters on the same line to extract candidate regions of the license plate. Experimental results show that the proposed method achieves higher accuracy, more acceptable and precise results. By using the data sets of the license plates in the USA and Europe, the method is shown to be suitable for multi-style license plates.