Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy

Kalyani, R.; Sathya, P. D.; Sakthivel, V.

doi:10.1016/j.jestch.2020.07.007

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…There are two types of thresholding processes: bi-level thresholding and multi-level thresholding. These approaches refer to how many levels the algorithm uses to divide the image into segments, rather than how it calculates the level between segments [57].…”

Section: Introductionmentioning

confidence: 99%

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Image Segmentation

Rather,

Das

2023

Mathematics

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Image segmentation is one of the pivotal steps in image processing due to its enormous application potential in medical image analysis, data mining, and pattern recognition. In fact, image segmentation is the process of splitting an image into multiple parts in order to provide detailed information on different aspects of the image. Traditional image segmentation techniques suffer from local minima and premature convergence issues when exploring complex search spaces. Additionally, these techniques also take considerable runtime to find the optimal pixels as the threshold levels are increased. Therefore, in order to overcome the computational overhead and convergence problems of the multilevel thresholding process, a robust optimizer, namely the Levy flight and Chaos theory-based Gravitational Search Algorithm (LCGSA), is employed to perform the segmentation of the COVID-19 chest CT scan images. In LCGSA, exploration is carried out by Levy flight, while chaotic maps guarantee the exploitation of the search space. Meanwhile, Kapur’s entropy method is utilized for segmenting the image into various regions based on the pixel intensity values. To investigate the segmentation performance of ten chaotic versions of LCGSA, firstly, several benchmark images from the USC-SIPI database are considered for the numerical analysis. Secondly, the applicability of LCGSA for solving real-world image processing problems is examined by using various COVID-19 chest CT scan imaging datasets from the Kaggle database. Further, an ablation study is carried out on different chest CT scan images by considering ground truth images. Moreover, various qualitative and quantitative metrics are used for the performance evaluation. The overall analysis of the experimental results indicated the efficient performance of LCGSA over other peer algorithms in terms of taking less computational time and providing optimal values for image quality metrics.

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Section: Introductionmentioning

confidence: 99%

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Image Segmentation

Rather,

Das

2023

Mathematics

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“…An adaptive Equilibrium Optimizer (EO) was also used for multilevel thresholding by minimizing interdependence [53]. Furthermore, the Exchange Market Optimization (EMO) method was used to segment images using the minimum cross-entropy thresholding approach [54]. Finally, Elazizi et al [55] used a hyper-heuristic strategy for multilevel image thresholding by maximizing between-class variance to overcome the constraints of meta-heuristic approaches.…”

Section: Introductionmentioning

confidence: 99%

Battle royale optimizer for multi-level image thresholding

Akan

Oliva

Marco

et al. 2023

Preprint

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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 grey 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 (PSNR), Structural Similarity Index Method (SSIM), Feature Similarity Index Method (FSIM), Color FSIM (FSIMc) and Standard Deviation (SD). The results of the proposed technique suggest that BRO is a promising approach for solving image segmentation tasks.

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“…10.3389/fninf.2023.1126783 Kalyani et al (2020) devised and evaluated an efficient exchange market algorithm for image segmentation utilizing the minimal cross-entropy thresholding approach on brain pictures with varying threshold values. Elaziz et al (2020) introduced an improved Harris hawks optimizer for global optimization and determining the best threshold values for MIS situations.…”

Section: Introductionmentioning

confidence: 99%

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

Zhao

Liu

Heidari

et al. 2023

Front. Neuroinform.

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The novel coronavirus pneumonia (COVID-19) is a respiratory disease of great concern in terms of its dissemination and severity, for which X-ray imaging-based diagnosis is one of the effective complementary diagnostic methods. It is essential to be able to separate and identify lesions from their pathology images regardless of the computer-aided diagnosis techniques. Therefore, image segmentation in the pre-processing stage of COVID-19 pathology images would be more helpful for effective analysis. In this paper, to achieve highly effective pre-processing of COVID-19 pathological images by using multi-threshold image segmentation (MIS), an enhanced version of ant colony optimization for continuous domains (MGACO) is first proposed. In MGACO, not only a new move strategy is introduced, but also the Cauchy-Gaussian fusion strategy is incorporated. It has been accelerated in terms of convergence speed and has significantly enhanced its ability to jump out of the local optimum. Furthermore, an MIS method (MGACO-MIS) based on MGACO is developed, where it applies the non-local means, 2D histogram as the basis, and employs 2D Kapur’s entropy as the fitness function. To demonstrate the performance of MGACO, we qualitatively analyze it in detail and compare it with other peers on 30 benchmark functions from IEEE CEC2014, which proves that it has a stronger capability of solving problems over the original ant colony optimization for continuous domains. To verify the segmentation effect of MGACO-MIS, we conducted a comparison experiment with eight other similar segmentation methods based on real pathology images of COVID-19 at different threshold levels. The final evaluation and analysis results fully demonstrate that the developed MGACO-MIS is sufficient to obtain high-quality segmentation results in the COVID-19 image segmentation and has stronger adaptability to different threshold levels than other methods. Therefore, it has been well-proven that MGACO is an excellent swarm intelligence optimization algorithm, and MGACO-MIS is also an excellent segmentation method.

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Trading strategies for image segmentation using multilevel thresholding aided with minimum cross entropy

Cited by 15 publications

References 26 publications

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Image Segmentation

Levy Flight and Chaos Theory-Based Gravitational Search Algorithm for Image Segmentation

Battle royale optimizer for multi-level image thresholding

An enhanced ant colony optimizer with Cauchy-Gaussian fusion and novel movement strategy for multi-threshold COVID-19 X-ray image segmentation

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