Image Threshold Segmentation Research Articles

Multi-Level Thresholding Color Image Segmentation Using Modified Gray Wolf Optimizer

The success of image segmentation is mainly dependent on the optimal choice of thresholds. Compared to bi-level thresholding, multi-level thresholding is a more time-consuming process, so this paper utilizes the gray wolf optimizer (GWO) algorithm to address this issue and enhance accuracy. To acquire the optimal thresholds at different levels, we modify the GWO (MGWO) in terms of leader selection, position update, and mutation. We also use the Otsu method and Kapur entropy as objective functions. The performance of MGWO is compared with other color image segmentation algorithms on ten images from the BSD500 dataset in terms of objective values, variance, signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and feature similarity index (FSIM). Experimental and non-parametric statistical analyses demonstrate that MGWO performs excellently in the multi-level thresholding segmentation of color images.

Improved snow ablation optimization for multilevel threshold image segmentation

Improved snow ablation optimization for multilevel threshold image segmentation

Energy curve based enhanced smell agent optimizer for optimal multilevel threshold selection of thermographic breast 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.

A complex-valued encoding golden jackal optimization for multilevel thresholding image segmentation

Multilevel thresholding image segmentation is not only an indispensable component of image computation and machine vision but also a fundamental element of image analysis and feature extraction, which has received extensive attention from many domestic and international academics in recent years. However, the threshold level rises in direct proportion to the computational complexity. Therefore, this paper presents a complex-valued encoding golden jackal optimization (CGJO) established on Kapur’s entropy to accomplish this issue, and the intention is to split the available image into a multitude of conspicuous salient portions that illustrate concepts and procedures of the pertinent object. The golden jackal optimization (GJO) employs the cooperated foraging of the jackals to imitate prey searching, enclosing and pouncing to generate the optimum solution. The complex-valued encoding employs the diploid’s notion to alter the real and imaginary of the search agent, which amplifies the information multiplicity and reinforces the inherent parallelism to motivate productive search and inhibit voracious convergence. The functionality and viability of the CGJO are confirmed by comparison with BWOA, DOA, COA, LCA, OOA, SABO, GJO and CRWOA. The experimental results reveal that the CGJO explores and exploits to acquire a superior convergence speed, greater numerical precision and stronger segmentation quality. Additionally, the CGJO offers excellent practicability and stability to address image segmentation successfully.

A Novel Optimization Algorithm for Otsu's Entropy-Based Multi-Level Thresholding for Image Segmentation

In applications involving image processing, segmentation is an essential stage. This procedure divides the image's pixels into various classes, enabling the examination of the scene's objects. Finding the ideal collection of thresholds to correctly segment each image is a challenge that multilevel thresholding solves with ease. The optimal thresholds can be found using methods like Otsu's between-class variance or kapur's entropy, but they are computationally costly when there are more than two thresholds. This study presents a novel meta-heuristic algorithm, Election-Based Optimization Algorithm (EBOA) to discover the optimal threshold configuration with Otsu as the objective function, to solve this kind of problem. The obtained results proved better in WPSNR, PSNR, SSIM, FSIM and misclassification error and segmented image quality when compared with existing algorithms.

Image Segmentation Model for Vicinagearth Security Technology of Unmanned Aerial Vehicle Using Improved Pigeon-Inspired Optimization

The vicinagearth security technology system covers a wide range of fields such as low-altitude security, underwater security, and cross-domain security. Among them, unmanned aerial vehicle (UAV) security will become one of the evolving forms of its security technology, and how to improve the segmentation and recognition ability of UAV visual reconnaissance system for maritime targets through improvement will become the key to low-altitude security. Due to the fact that maritime target images are characterized by complex weather, strong interference, high speed requirement and large data volume, the traditional segmentation methods are not suitable for maritime small-target (MST) segmentation and recognition. Therefore, this paper proposes a threshold image segmentation (TIS) method based on an improved pigeon-inspired optimization (PIO) algorithm to provide a better method for segmentation and recognition of MST. First, this study proposes CCPIO based on the horizontal crossover search (HCS) and vertical crossover search (VCS) strategy, which effectively improves the search efficiency of PIO and the ability to jump out of local optimum. And the optimization performance of CCPIO is effectively verified by comparing it with 10 peer algorithms through benchmark function experiments. Further, in this paper, the proposed CCPIO-TIS segmentation model is proposed by combining CCPIO with non-local means, 2D histogram, and Kapur’s entropy. The proposed CCPIO-TIS model is also used for the segmentation and recognition of real MST images, and the results of the experimental comparison and evaluation analysis show that the proposed model has higher quality segmentation results than 12 models of the same type. In summary, this study can provide an efficient and accurate artificial intelligence model for segmentation and recognition of maritime small-target.

Modified snake optimizer based multi-level thresholding for color image segmentation of agricultural diseases

Image segmentation is crucial for early identification and diagnosis of crop diseases in agriculture. It helps identify disease areas and characteristics, laying the groundwork for disease diagnosis and treatment. However, color image thresholding segmentation method faces challenges in finding the optimal threshold as the number of thresholds increases, which affects segmentation quality. In this paper, a modified snake optimizer (MSO) is proposed to address the color image thresholding segmentation problem using Kapur’s entropy as the objective function. MSO incorporates a dynamic adaptive parameter adjustment method. The improved global position update formula introduces guidance from the optimal individual and disturbance from random individuals, effectively achieving a balance between global and local search capabilities. A dynamic parameter adjustment method is added to accelerate convergence speed during snake movement to food. Lévy flight is introduced to the Flight mode to help the algorithm escape local extremums. A balancing strategy is implemented in the Mating mode, incorporating guidance from the optimal individual and information exchange between sub-populations, enabling balanced exploration and local exploitation capabilities. The hill-climbing jump operation for the optimal individual is added to help the algorithm overcome local stagnation. The proposed MSO is evaluated on CEC 2017 test functions and color test images, and the obtained results are compared with other segmentation methods and other intelligent optimization algorithms. The results demonstrate that MSO exhibits stable and excellent performance in both complex global optimization problems and color image thresholding segmentation problems. Finally, MSO is applied to segment rice disease images, and the results reveal significantly better segmentation performance compared to other algorithms. MSO shows great potential in solving the thresholding segmentation problem for agricultural disease images, thereby assisting in the accurate identification, diagnosis, and treatment of agricultural diseases and insect pests.

Enhancing image thresholding segmentation with a novel hybrid battle royale optimization algorithm

Enhancing image thresholding segmentation with a novel hybrid battle royale optimization algorithm

DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation

Colon adenocarcinoma (COAD) is a type of colon cancers with a high mortality rate. Its early symptoms are not obvious, and its late stage is accompanied by various complications that seriously endanger patients' lives. To assist in the early diagnosis of COAD and improve the detection efficiency of COAD, this paper proposes a multi-level threshold image segmentation (MIS) method based on an enhanced particle swarm algorithm for segmenting COAD images. Firstly, this paper proposes a multi-strategy fusion particle swarm optimization algorithm (DRPSO) with a replacement mechanism. The non-linear inertia weight and sine-cosine learning factors in DRPSO help balance the exploration and exploitation phases of the algorithm. The population reorganization strategy incorporating MGO enhances population diversity and effectively prevents the algorithm from stagnating prematurely. The mutation-based final replacement mechanism enhances the algorithm's ability to escape local optima and helps the algorithm to obtain highly accurate solutions. In addition, comparison experiments on the CEC2020 and CEC2022 test sets show that DRPSO outperforms other state-of-the-art algorithms in terms of convergence accuracy and speed. Secondly, by combining the non-local mean 2D histogram and 2D Renyi entropy, this paper proposes a DRPSO algorithm based MIS method, which is successfully applied to the segments the COAD pathology image problem. The results of segmentation experiments show that the above method obtains relatively higher quality segmented images with superior performance metrics: PSNR = 23.556, SSIM = 0.825, and FSIM = 0.922. In conclusion, the MIS method based on the DRPSO algorithm shows great potential in assisting COAD diagnosis and in pathology image segmentation.

Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image segmentation methods are favored for their computational simplicity and operational efficiency. Existing threshold selection techniques in multi-threshold image segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency and accuracy. The whale optimization algorithm (WOA) has shown promise in addressing this challenge, but issues persist, including poor stability, low efficiency, and accuracy in COVID-19 threshold image segmentation. To tackle these issues, we introduce a Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates a COS sampling initialization strategy (COSI), an adaptive global search approach (GS), and an all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from Latin hypercube sampling, ensuring even solution space coverage to improve the stability of the segmentation model. GS widens the exploration scope to combat stagnation during iterations and improve segmentation efficiency. ADN refines convergence accuracy around optimal individuals to improve segmentation accuracy. CAGWOA's performance is validated through experiments on various benchmark function test sets. Furthermore, we apply CAGWOA alongside similar methods in a multi-threshold image segmentation model for comparative experiments on lung X-ray images of infected patients. The results demonstrate CAGWOA's superiority, including better image detail preservation, clear segmentation boundaries, and adaptability across different threshold levels.

Optimal Whole Slide Image Segmentation Using Generalized Normal Distribution Optimization

Whole slide image (WSI) segmentation is a crucial task aiding tumour and cancerous cell diagnosis. Generalized Normal Distribution Optimization (GNDO) algorithm is adopted for whole slide image segmentation based on thresholding in this paper. GNDO algorithm utilizes the generalized normal distribution's properties to determine the ideal thresholds for image segmentation. Through various metrics, the efficacy of GNDO in comparison to traditional Otsu thresholding methods is demonstrated. As demonstrated by the results, it can offer reliable and flexible solutions for different histopathology images.

Improving the estimation of distribution algorithm with a differential mutation for multilevel thresholding image segmentation

Improving the estimation of distribution algorithm with a differential mutation for multilevel thresholding image segmentation

Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm.

Cyclone Intensity Estimation System using Satellite Images

Tropical cyclones are big storms that can cause a lot of damage. Cyclone intensity estimation plays a vital role in disaster preparedness, response, and mitigation strategies. This paper introduces a novel approach to estimating cyclone intensity using satellite images through a Convolutional Neural Network (CNN) model. Unlike previous methods, we employ advanced techniques such as histogram analysis for feature extraction and adaptive thresholding for image segmentation using mean, Gaussian, and Otsu methods. The model also predicts potential coverage distance. Additionally, we present a user-friendly visualization portal, a pioneering effort in this field, which displays the deep learning output along with contextual information for end-users

Chaotic opposition Golden Sinus Algorithm for global optimization problems

Optimization techniques are required to find the best solutions to challenging problems in many engineering disciplines. Metaheuristic algorithms that effectively increase search performance using various evolutionary strategies have become increasingly popular in recent years. The Golden Sinus Algorithm (GoldSa) is a population-based optimization algorithm that uses the sine function and the golden ratio. In this study, a chaotically enhanced opposition-based Golden Sinus Algorithm (Co-GoldSa) has been proposed to improve the efficiency of the exploitation and exploration ability of the GoldSa method. While designing this approach, it is first necessary to analyze the chaotic behavior of the GoldSa parameters. For this purpose, three chaotic GoldSa methods have been developed using eight chaotic maps to determine the effect of the chaotic maps on the parameters with different behaviors. Secondly, the opposition-based learning strategy is adjusted to the cGoldSa to enhance the searching ability. To investigate the proficiency of the proposed Co-GoldSa method, it has been examined with well-known and newly introduced metaheuristic approaches on benchmark functions and classical engineering design problems. Besides, an efficient framework of the multilevel thresholding image segmentation has been presented based on the Co-GoldSa method since the efficient processing of pathological images is quite important in medical diagnostics. The experimental outcomes reveal the superiority of the proposed method in solving global optimization problems, image segmentation, and engineering problems. Thus, the outcomes of the benchmark functions, image segmentation, and classical engineering problems support that the proposed Co-GoldSa approach can be considered a promising method for resolving challenging optimization problems.

A shoreline detection method with X-band marine radar

The assessment of morphological mapping capacity of X-band radars is performed by comparing radar-derived shoreline estimates with in-situ data measured from DGPS-RTK surveys in Cala Millor beach (October 22–24, 2018). Radar estimates are obtained by applying an automatic method based on digital image processing, threshold segmentation and first-order derivative filters on X-Band radar variance images. The study site is covered by the endemic Posidonia oceanica seagrass meadow at the seabed. It represents a challenge on the estimation of local shoreline. Results reveal that X-band radar estimate the shoreline location with a mean bias of 4.66 m, a relative error of 2.95% and a RMSE of 14.07 m. Also, the beach morphology is assessed by the identification of surrounding rocky outcrops and a submerged sandbar. Since the obtained radar estimation errors are comparable with the reported by previous studies but using video-cameras, results reveal a potential complementary use of both marine radars and optical cameras to provide higher-resolution and more accurate shoreline measurements in a broader area.

Research on Human Lung Impedance Tomography Based on Soft Thresholding Image Segmentation and Reduced-Order Tikhonov Regularization

The lung is one of the most vital organs in the human body, and its condition is closely correlated with overall health. Electrical impedance tomography (EIT), as a biomedical imaging technique, often produces low-quality reconstructed images due to its inherent ill-posedness in solving the inverse problem. To address this issue, this paper proposes a soft-threshold region segmentation algorithm with a relaxation factor. This algorithm segments the reconstructed lung images into internal regions, edge regions, and background regions, resulting in clearer boundaries in the reconstructed images. This facilitates the intuitive identification of regions of interest by healthcare professionals. Additionally, this segmentation algorithm is suitably combined with a dimension-reduced Tikhonov regularization algorithm. By utilizing the joint capabilities of these algorithms, the partition points belonging to the background region can be excluded from the sought grayscale vector, thereby improving the ill-posedness of the image reconstruction process and enhancing the quality of image reconstruction. Finally, a 16-electrode human lung EIT simulation model is established for the thoracic region and verified through simulation. Experimental validation is conducted using a human lung tank simulation platform to further demonstrate the effectiveness of the proposed method.

A Resampling Ant Colony Optimization with Elite Exploration and Convergence Mechanism for Multithreshold Segmentation of Breast Cancer Images

Previous studies have emphasized the potential of threshold image segmentation for early breast cancer detection. However, traditional methods encounter challenges regarding low segmentation efficiency and accuracy. Addressing this, the ant colony optimization algorithm for continuous optimization (ACOR) shows promise. Yet, existing ACOR variants still grapple with poor initial population quality, affecting convergence speed and avoiding local optimization. These issues impact segmentation efficiency and accuracy. To tackle them, this study introduces RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local optima entrapment and enhancing model stability. SCM expediting convergence, segmentation efficiency, and precision. RESACO's performance is assessed through extensive experiments using IEEE CEC 2014 and IEEE CEC 2022 benchmark functions, including ablation experiments and comparisons with basic and improved algorithms and ACOR variants. Subsequently, the threshold image segmentation model based on RESACO is compared with other models using metaheuristic algorithms for segmenting realistic breast cancer medical images. Results demonstrate the proposed model's faster convergence and higher segmentation accuracy, preserving more lesion tissue details.

Multilevel thresholding with divergence measure and improved particle swarm optimization algorithm for crack image segmentation

Crack formation is a common phenomenon in engineering structures, which can cause serious damage to the safety and health of these structures. An important method of ensuring the safety and health of engineered structures is the prompt detection of cracks. Image threshold segmentation based on machine vision is a crucial technology for crack detection. Threshold segmentation can separate the crack area from the background, providing convenience for more accurate measurement and evaluation of the crack condition and location. The segmentation of cracks in complex scenes is a challenging task, and this goal can be achieved by means of multilevel thresholding. The arithmetic-geometric divergence combines the advantages of the arithmetic mean and the geometric mean in probability measures, enabling a more precise capture of the local features of an image in image processing. In this paper, a multilevel thresholding method for crack image segmentation based on the minimum arithmetic-geometric divergence is proposed. To address the issue of time complexity in multilevel thresholding, an enhanced particle swarm optimization algorithm with local stochastic perturbation is proposed. In crack detection, the thresholding criterion function based on the minimum arithmetic-geometric divergence can adaptively determine the thresholds according to the distribution characteristics of pixel values in the image. The proposed enhanced particle swarm optimization algorithm can increase the diversity of candidate solutions and enhance the global convergence performance of the algorithm. The proposed method for crack image segmentation is compared with seven state-of-the-art multilevel thresholding methods based on several metrics, including RMSE, PSNR, SSIM, FSIM, and computation time. The experimental results show that the proposed method outperforms several competing methods in terms of these metrics.

Correction: Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation

Correction: Boosted Aquila Arithmetic Optimization Algorithm for multi-level thresholding image segmentation