Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

Su, Hang; Zhao, Dong; Fu, Yu; Heidari, Ali Asghar; Zhang, Yu; Chen, Huiling; Li, Chengye; Pan, Jingye; Quan, Shichao

doi:10.1016/j.compbiomed.2021.105181

Cited by 62 publications

(22 citation statements)

References 119 publications

(126 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Joshi et al [ 47 ] proposed a new saliency-based region detection and image segmentation model for COVID-19 X-ray image segmentation. Su et al [ 48 ] proposed a multilevel thresholding image segmentation model that combines an improved artificial bee colony algorithm for COVID-19 X-ray images. Zhao et al [ 49 ] presented a novel ant colony algorithm (ACO), which combined horizontal crossover search and vertical crossover search to optimize multithreshold image segmentation.…”

Section: Related Workmentioning

confidence: 99%

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Zhao

et al. 2022

Computers in Biology and Medicine

Self Cite

154

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Zhao

et al. 2022

Computers in Biology and Medicine

Self Cite

154

View full text Add to dashboard Cite

“…Su et al [58] presented a variant ABC, namely CCABC, which introduced vertical search and horizontal search mechanisms to improve ABC's optimization performance. Furthermore, the proposed method, CCABC, was used to find the appropriate threshold values in COVID-19 X-ray images based on Kapur's entropy.…”

Section: Related Workmentioning

confidence: 99%

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

Liu

Jia

et al. 2022

Mathematics

View full text Add to dashboard Cite

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.

show abstract

“…Recent studies on the automatic segmentation of medical images have concentrated on the combined use of traditional and machine learning to achieve this goal. Su et al [ 6 ] efficiently improved the segmentation performance of COVID-19 lesions through a multilevel thresholding image segmentation method. Liu et al [ 7 ] applied a multilevel segmentation model based on modified differential evolution to segment breast cancer images.…”

Section: Related Workmentioning

confidence: 99%

HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution

Chen

Zhou

Chen

et al. 2022

Computers in Biology and Medicine

View full text Add to dashboard Cite

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images

Cited by 62 publications

References 119 publications

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

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

HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution

Contact Info

Product

Resources

About