Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images.
Comput Biol Med
; 142: 105181, 2022 03.
Article
in English
| MEDLINE | ID: covidwho-1588026
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Image Processing, Computer-Assisted
/
COVID-19
Type of study:
Experimental Studies
Limits:
Humans
Language:
English
Journal:
Comput Biol Med
Year:
2022
Document Type:
Article
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