Guided fireworks algorithm applied to multilevel image thresholding

Multilevel thresholding is one of the most widely used techniques for image segmentation. A thresholding technique for image segmentation is mainly categorized into two types such as bi-level and multilevel thresholding. A single threshold value is used in bi-level thresholding for image classification such as—foreground object and background object. Bi-level thresholding gives unsatisfactory segmentation results in case of complex image; hence, the idea of multilevel thresholding has been preferred over bi-level thresholding method. In multilevel thresholding, selection of threshold values mostly gives inaccurate values, and it is a time-consuming process. Hence, automatic multilevel thresholding techniques are used as an objective functions to choose optimal threshold values but faces high computational complexity problems. Meta-heuristic algorithms play an important role to reduce the computational complexity of multilevel thresholding. In this paper, we have surveyed various objective functions used in automatic multilevel thresholding and performed a comparative study about the performances of some recent meta-heuristic algorithms, which are widely used in multilevel thresholding. Also, discussed different datasets and metrics used to evaluate multilevel thresholding techniques. In addition, some applications of image segmentation are also discussed.KeywordsParametricNonparametricMeta-heuristicsOptimization

Hybrid leader corona virus herd optimizer with multilevel thresholding techniques for foreground and background image segmentation

Multilevel thresholding is a significant technology for image segmentation. Traditional exhaustive search method to solve the optimal multilevel thresholds is time consuming, color images contain more information are even worse. To address this issue, an improved cuckoo search algorithm (ICS) is proposed for color image segmentation in this paper, and a modified fuzzy entropy is used as its objective function. In ICS, two modifications are adopted to improve the standard cuckoo search algorithm. First, an adaptive control parameter mechanism is utilized to enhance the performance of exploration. Second, a hybrid search strategy is used to boost the local search efficiency. To fully demonstrate the superior performance of the ICS, the experiments are conducted on a series of color benchmark test images, and a total of six optimization algorithms are compared with the proposed algorithm. The experimental results show that the presented ICS algorithm outperforms all the other algorithms in term of objective function value, PSNR, FSIM, convergence speed and parametric statistical test. Compared to other algorithms, the ICS algorithm is an effective method for multilevel color image thresholding segmentation.

Multilevel thresholding is a widely used image segmentation technique. However, multilevel thresholding becomes more and more computationally expensive as the number of thresholds increase. Therefore, it is essential to incorporate some suitable optimization technique to make it practical. In this article, a modification is proposed to the Moth-Flame Optimization (MFO) algorithm and then it is applied to multilevel thresholding for image segmentation. Cross entropy is used as the objective function to select the optimal thresholds. A set of benchmark test images are used to evaluate the proposed technique. The Mean Structural SIMilarity (MSSIM) index is used to measure the quality of the segmented images. The results of the proposed technique are compared with the original MFO, PSO, BFO, and WOA. Experimental results and analysis suggest that the proposed technique outperforms other techniques in terms of segmentation quality images and stability. Moreover, computation time required for multilevel thresholding is also reduced to a manageable level.

Multilevel thresholding is an effective and indispensable technology for image segmentation that has attracted extensive attention in recent years. However, the multilevel thresholding method has some disadvantages, such as a large computational complexity and low segmentation accuracy. Therefore, this paper proposes a whale optimization algorithm (WOA) based on Kapur's entropy method to solve the image segmentation problem. The WOA can effectively balance exploration and exploitation to avoid falling into premature convergence and obtain the global optimal solution. To verify the segmentation performance of the WOA, a series of experiments on underwater images from the experimental pool of Harbin Engineering University are conducted, and the segmentation results are compared with those of the BA, the FPA, MFO, the MSA, PSO and WWO by maximizing the fitness value of Kapur's entropy method. The fitness value, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), execution time and Wilcoxon's rank-sum test are used to evaluate the overall performance of each algorithm. The experimental results reveal that the WOA is superior to the other comparison algorithms and has a higher segmentation accuracy, better segmentation effect and stronger robustness. In addition, the feasibility and efficiency of the WOA are verified.

Modified bacterial foraging algorithm based multilevel thresholding for image segmentation

The satellite image segmentation is a key area for current research and numerous work has been done for exploration of this area. The nature inspired optimization algorithms are very promising with image segmentation techniques to provide a platform for processing of satellite images. In this paper a literature review of different nature based optimization algorithms such as modified artificial bee colony (MABC) algorithm, ABC algorithm, particle swarm optimization (PSO), Darwinian PSO, genetic algorithm (GA), Wind driven optimization (WDO) and cuckoo search(CS) using different objective functions has been discussed to find the optimized multilevel thresholds. These nature influenced optimization methods and their performances are compared using different objective functions for optimal multilevel thresholding. The comparative study shows that the MABC algorithm and different variants of CS algorithm are very strong and accurate in results generating using image segmentation. Both methods search multilevel thresholds very efficiently and correctly, and in MABC an improved bee’s search solution are used and in CS the cuckoo search solution are used.

The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

The digital image segmentation is an open problem that is growing day by day and is attracting the attention of researchers from last few years. Image resolution and their speed has led to the use of thresholding approaches. Image thresholding is simple, easy and effective method for image segmentation. Multi-level image thresholding is a key perspective in several real-time pattern recognition and image processing-based applications. It identifies pixels quickly and effectively in different groups indicating multiple regions in an image. Segmentation of images based on thresholding by using various intelligent optimization techniques with fuzzy entropy is widely utilized for defining thresholds in a better way to use them precisely. In this research, a novel technique for multi-level thresholding is proposed by combining Fuzzy Entropy Type II (FE-TII) with recently developed meta-heuristics named Marine Predators Algorithm (MPA). For achieving optimal thresholds of an image, the maximization of entropy is tedious and consumes a lot of time with an increasing number of thresholds. The MPA method presented is analyzed in context with image segmentation, particularly on thresholds with TII-FE. For this reason, proposed methodology is evaluated using several images along with the distribution of histograms. For analyzing the performance efficiency of the proposed methodology, the results are compared and robustness is tested with efficiency of proposed technique to multi-level image segmentation, several images are used randomly from datasets.

Image segmentation is a prime operation to understand the content of images. Multilevel thresholding is applied in image segmentation because of its speed and accuracy. In this paper, a novel multilevel thresholding algorithm based on differential evolution (DE) search is introduced. One of the major drawbacks of metaheuristic algorithms is the stagnation phenomenon which leads to falling into local optimums and premature convergence. To overcome this shortcoming, the idea of Darwinian theory is incorporated with DE algorithm to increase the diversity and quality of the individuals without decreasing the convergence speed of DE algorithm. A policy of encouragement and punishment is considered to lead searching agents in the search space and reduce the computational time. The algorithm is implemented based on dividing the population into specified groups and each group tries to find a better location. Ten test images are selected to verify the ability of our algorithm using the famous energy curve method. Kapur entropy and Type 2 fuzzy entropy are employed to evaluate the capability of the introduced algorithm. Nine different metaheuristic algorithms with Darwinian modes are also implemented and compared with our method. Experimental results manifest that the proposed method is a powerful tool for multilevel thresholding and the obtained results outperform the DE algorithm and other methods.

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

Multilevel thresholding image segmentation will subdivide an image into several meaningful regions or objects, which makes the image more informative and easier to analyze. Optimal multilevel thresholding approaches are extensively used for segmentation because they are easy to implement and offer low computational cost. Multilevel thresholding image segmentation is frequently performed using popular methods such as Otsu’s between-class variance and Kapur’s entropy. Numerous researchers have used evolutionary algorithms to identify the best multilevel thresholds based on the above approaches using histogram. This paper uses the Energy Curve (EC) based thresholding method instead of the histogram. Chaotic Bidirectional Smell Agent Optimization with Adaptive Control Strategy (ChBSAOACS), a powerful evolutionary algorithm, is developed and employed in this paper to create and execute an effective method for multilevel thresholding segmentation of breast thermogram images based on energy curves. The proposed algorithm was tested for viability on standard breast thermogram images. All experimental data are examined quantitatively and qualitatively to verify the suggested method’s efficacy.

Image segmentation is necessity of many application like brain tumor detection, optical character recognition, thermal energy leakage detection, Face recognition etc. multilevel thresholding is the complex task to find optimal threshold. Entropy plays an important role for efficient segmentation. We have introduced entropy-based thresholding with metaheuristic approach to find optimal threshold for gray images. Firefly Algorithm has been used with Kapur/Tsallis entropies to segment the image. All the methods have intent to find optimal threshold, based on experimental results Tsallis method offer better PSNR and SSIM values and capable of effective segmentation of images.

Optimal multilevel thresholding using bacterial foraging algorithm

We propose a fast PNN-based <i>O</i>(<i>N log N</i>) time algorithm for multilevel non-parametric thresholding, where N denotes the size of the image histogram. The proposed PNN based multilevel thresholding algorithm is considerably faster than optimal thresholding. On a set of 8-16 bits per pixel real images, experimental results also reveal that the proposed method provides better quality than the Lloyd-Max quantizer alone. Since the time complexity of the proposed thresholding algorithm is log-linear, it is applicable in real-time image processing applications.