Image segmentation using multilevel thresholding based on modified bird mating optimization

Modified bacterial foraging algorithm based multilevel thresholding for image segmentation

Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu's and Kapur's methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques were introduced for bi-level thresholding. However, these techniques can be expanded to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. To this end, various optimization techniques have been used to overcome this drawback. Recently, a new optimization algorithm called Battle Royal Optimizer (BRO) has been published, which is shown to solve various optimization tasks effectively. In this study, BRO has been applied to yield optimum threshold values in multilevel image thresholding. Here is also demonstrated the effectiveness of BRO for image segmentation on various images from the standard publicly accessible Berkeley segmentation dataset. We compare the performance of BRO to other state-of-the-art optimization-based methods and show that it outperforms them in terms of fitness value, Peak Signal-to-Noise Ratio, Structural Similarity Index Method, Feature Similarity Index Method (FSIM), Color FSIM (FSIMc), and Standard Deviation. These results underscore the potential of BRO as a promising solution for image segmentation tasks, particularly through its effective implementation of multilevel thresholding.

Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel 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.

Segmentation is low-level image transformation routine that partitions an input image into distinct disjoint and homogeneous regions using thresholding algorithms. This paper presents both adaptation and comparison of four stochastic optimisation techniques to solve multilevel thresholding problem in image segmentation: Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Bacterial Foraging (BF) and Modified BF (MBF). Three objective functions such as Tsallis, Kapur’s and Otsu’s functions are considered and maximised by the above four algorithms. In order to compare the performances of all the algorithms, they are tested on various test images. Results show that the BF and MBF are much better in terms of robustness and time convergence than the PSO and GA. Among the last two algorithms, MBF is the most efficient with respect to the quality of the solution in terms of Peak Signal to Noise Ratio (PSNR) value and stability.

Image segmentation requires optimum multilevel threshold values obtained from the image in order to partition it into multiple regions. Estimating these thresholds poses a great challenge. In this paper, we propose a novel swarm intelligence technique, namely Bi-level Artificial Bee Colony (BABC) algorithm, to obtain the optimum thresholds by using the Tsallis Entropy as an objective function. BABC is used, along with a Sinusoidal Evaluation of Fitness Function (SEFF), to ensure that all the threshold values of the image are examined before arriving at the best possible solution. Experimental results show the promising performance of BABC for image segmentation as compared to other optimization algorithms like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bacterial Foraging (BF) Algorithm.

A hybrid differential evolution for optimal multilevel image thresholding

There has been much work on the numerical optimization of proton exchange membrane fuel cells (PEMFCs). The optimization techniques can be divided in heuristic and non-heuristic techniques. Heuristic techniques (such as genetic algorithm, particle swarm optimization, differential evolution, simulating annealing, harmony search, memetic algorithms, bird mating optimization, and artificial immune system) usually use stochastic methods to identify an optimal solution, which require a large number of device simulations and compromise the accuracy of the solution. The computational cost of heuristic techniques limit their application to a small number of variables (usually under ten), and can generally be applied to simplified models. In contrast, non-heuristic techniques (such as line search and trust-region methods, the Nelder-Mead method, the Powell algorithm, steepest descents, conjugate gradient methods, Newton and quasi-Newton methods, and linear and quadratic programming) are deterministic and often require computing or approximating the gradient of the objective function [1-4]. Usually these techniques have local convergence and their rate of convergence can be improved by using the Hessian matrix or other second-order derivatives, evaluating the gradient more accurately, or taking advantage of the particular functional form of the objective function. Depending on whether the gradient function can be evaluated or not, as well as on the accuracy with which the first and higher order derivatives can be computed, non-heuristic techniques usually require between a few and 100 iterations to optimize the objective function, while the total number of variables to be optimized could be larger than 105. In this work we compare heuristic and nonheuristic methods and present a gradient-based technique for the optimization of the catalyst distribution in PEMFCs using finite element models. We demonstrate a non-heuristic technique to determine optimum platinum distribution in the catalyst layer (CL) by allowing the catalyst amount to vary independently at each not inside the CL. Therefore, the total number of finite elements in the CL, in this work, is larger than 104; however, the technique could be extended to systems with even more elements. Optimization is performed by first computing a catalyst sensitivity function, which is the gradient of the objective function with respect to the mass of catalyst at each location in the CL; then an adjoint space technique is employed to evaluate the catalyst sensitivity functions at each node in the finite element discretization. Cell performance is evaluated and compared for the optimized and non-optimized PEMFCs.

Digital images are widely used and numerous application in different scientific fields use digital image processing algorithms where image segmentation is a common task. Thresholding represents one technique for solving that task and Kapur's and Otsu's methods are well known criteria often used for selecting thresholds. Finding optimal threshold values represents a hard optimization problem and swarm intelligence algorithms have been successfully used for solving such problems. In this paper we adjusted recent elephant herding optimization algorithm for multilevel thresholding by Kapur's and Otsu's method. Performance was tested on standard benchmark images and compared with four other swarm intelligence algorithms. Elephant herding optimization algorithm outperformed other approaches from literature and it was more robust.

The rapid advance of artificial intelligence has made complex image processing in real time possible. Multilevel thresholding has become a feasible way for image segmentation, even in the presence of poor contrast and external artefacts. Genetic algorithms (GAs) and particle swarm optimisation (PSO) are broadly recognised by far to be two dominating schemes which outperform classical ones on multilevel thresholding. Qualitative analysis can usually be applied to observe their superiority to all classical approaches. However, no convincing result is reached with respect to differences in performance between GAs and PSO. The existing segmentation practices are either examined by visual appeals exclusively, or evaluated quantitatively assuming perfect statistical distributions. To make thorough comparisons, comparative analysis of two leading multilevel thresholding approaches is conducted for true colour image segmentation. The information theory is also employed to analyse the outcomes of systematic approaches using diverse quantitative metrics from various aspects.

Optimal multilevel thresholding using bacterial foraging algorithm

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.

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.

Real-world optimization problems in engineering, logistics, and resource allocation are often constrained and multi-modal, posing a challenge for traditional optimization algorithms. Metaheuristic algorithms inspired by natural and artificial phenomena have shown promise, but many fail to balance exploration and exploitation effectively, especially under stringent constraints. Existing algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) face issues in convergence speed and constraint handling, particularly in high-dimensional spaces or when constraints are dynamic or complex. We propose an Adaptive Pattern-Driven Optimization (APDO) algorithm, a novel tailor-inspired metaheuristic that mimics the adaptive decision-making process of a tailor designing garments. APDO integrates three primary operators—Pattern Selection, Fabric Adjustment, and Stitch Reinforcement—to handle constraints adaptively. The algorithm combines pattern memory (historical bests), probabilistic pattern mutation, and a constraint-domination principle to ensure feasibility and diversity. The core idea is to iteratively “cut and stitch” solutions to adapt the search process, enabling dynamic constraint satisfaction and global optimization. We benchmarked APDO against five popular methods (GA, PSO, DE, Firefly Algorithm, and Whale Optimization Algorithm) on a suite of 10 real-world constrained problems, including mechanical component design and energy scheduling tasks. APDO outperformed all baselines in terms of convergence speed, constraint satisfaction rate, and solution quality. In particular, APDO achieved an average feasibility rate of 97.6% and an improvement of 4.2–11.8% in best fitness across problems.

Particle Swarm Optimization or Differential Evolution—A comparison