Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

Ewees, Ahmed A.; Abualigah, Laith; Yousri, Dalia; Sahlol, Ahmed T.; Al-qaness, Mohammed A. A.; Alshathri, Samah; Elaziz, Mohamed Abd

doi:10.3390/math9192363

Cited by 28 publications

(3 citation statements)

References 29 publications

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“…The application of the technique is evolving more favored, and it has been used in diverse fields. Image segmentation is required when element selection or hunting of image knowledge is required [ 39 ]. Image segmentation is an algorithm that separates the image into some disjoint areas by characteristics such as gray, colored, spatial surface and geometric form.…”

Section: Related Workmentioning

confidence: 99%

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

et al. 2023

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This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

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Section: Related Workmentioning

confidence: 99%

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

et al. 2023

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“…Image segmentation is usually used to detect objects and boundaries (curves, lines, edge, color, intensity, or texture) in images [84][85][86]. The purpose is to re-represent an image in a more meaningful and easier-to-interpret manner.…”

Section: Image Segmentationmentioning

confidence: 99%

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Gharaibeh

Alzu’bi

Abdullah

et al. 2022

BDCC

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Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.

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“…Ewees et al [56] proposed a new multi-level thresholding method, using modified artificial ecosystem-based optimization (AEO), namely AEODE. The AEODE integrated differential evolution as a local search operator to overcome AEO's shortcomings.…”

Section: Related Workmentioning

confidence: 99%

Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

Liu

Jia

et al. 2022

Mathematics

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Image segmentation is a key stage in image processing because it simplifies the representation of the image and facilitates subsequent analysis. The multi-level thresholding image segmentation technique is considered one of the most popular methods because it is efficient and straightforward. Many relative works use meta-heuristic algorithms (MAs) to determine threshold values, but they have issues such as poor convergence accuracy and stagnation into local optimal solutions. Therefore, to alleviate these shortcomings, in this paper, we present a modified remora optimization algorithm (MROA) for global optimization and image segmentation tasks. We used Brownian motion to promote the exploration ability of ROA and provide a greater opportunity to find the optimal solution. Second, lens opposition-based learning is introduced to enhance the ability of search agents to jump out of the local optimal solution. To substantiate the performance of MROA, we first used 23 benchmark functions to evaluate the performance. We compared it with seven well-known algorithms regarding optimization accuracy, convergence speed, and significant difference. Subsequently, we tested the segmentation quality of MORA on eight grayscale images with cross-entropy as the objective function. The experimental metrics include peak signal-to-noise ratio (PSNR), structure similarity (SSIM), and feature similarity (FSIM). A series of experimental results have proved that the MROA has significant advantages among the compared algorithms. Consequently, the proposed MROA is a promising method for global optimization problems and image segmentation.

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Modified Artificial Ecosystem-Based Optimization for Multilevel Thresholding Image Segmentation

Cited by 28 publications

References 29 publications

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Modified Remora Optimization Algorithm for Global Optimization and Multilevel Thresholding Image Segmentation

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