Paper Info
Title
Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation
Abstract
This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image segmentation technology. We present a new multilevel image segmentation method based on the swarm intelligence algorithm (SIA) to enhance the image segmentation of COVID-19 X-rays. This paper first introduces an improved ant colony optimization algorithm, and later details the directional crossover (DX) and directional mutation (DM) strategy, XMACO. The DX strategy improves the quality of the population search, which enhances the convergence speed of the algorithm. The DM strategy increases the diversity of the population to jump out of the local optima (LO). Furthermore, we design the image segmentation model (MIS-XMACO) by incorporating two-dimensional (2D) histograms, 2D Kapur's entropy, and a nonlocal mean strategy, and we apply this model to COVID-19 X-ray image segmentation. Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. Other SIAs and image segmentation models were used to ensure the validity of the experiments. The proposed MIS-XMACO model shows more stable and superior segmentation results than other models at different threshold levels by analyzing the experimental results.
Year
DOI
Venue
2022
10.1016/j.compbiomed.2022.105810
Computers in Biology and Medicine
Keywords
DocType
Volume
COVID-19 X-ray,Ant colony optimization,Image segmentation,Swarm intelligence,ACO,Optimization
Journal
148
ISSN
Citations 
PageRank 
0010-4825
1
0.43
References 
Authors
41
10
Name
Order
Citations
PageRank
Ailiang Qi110.43
Dong Zhao211.11
Fanhua Yu310.43
Ali Asghar Heidari437923.01
Zongda Wu510.43
Zhennao Cai611.11
Fayadh Alenezi713.14
Romany F Mansour810.43
Huiling Chen940228.49
Mayun Chen1010.43