Paper Info
Title
Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images
Abstract
The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.
Year
DOI
Venue
2022
10.1016/j.compbiomed.2021.105181
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Disease diagnosis, Multi-threshold image segmentation, Meta-heuristic, COVID-19, Swarm-intelligence
Journal
142
ISSN
Citations 
PageRank 
0010-4825
1
0.35
References 
Authors
0
9
Name
Order
Citations
PageRank
Hang Su130.71
Dong Zhao231.72
Fanhua Yu331.37
Ali Asghar Heidari441.38
Yu Zhang510.35
Huiling Chen6121.80
Chengye Li71386.88
Jingye Pan820.69
Shichao Quan910.35