Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 148, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2022.105810
Keywords
COVID-19 X-ray; Ant colony optimization; Image segmentation; Swarm intelligence; ACO; Optimization
Categories
Funding
- National Natural Science Foundation of China [62076185, U1809209]
- Key Project of Zhejiang Provincial Natural Science Foundation [LZ20F020004]
- Science and Technology Project of Jilin Provincial Depart- ment of Education [JJKH20200829KJ]
- Science and Technology Research Project of Jilin Provincial Education Department [JJKH20210888KJ]
- Changchun Normal University Ph.D. Research Startup Funding Project [BS [2020]]
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This paper focuses on the study of COVID-19 X-ray image segmentation technology. A new multilevel image segmentation method based on the swarm intelligence algorithm is proposed, along with a designed image segmentation model. Experimental results show that the proposed model achieves more stable and superior segmentation results at different threshold levels.
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.
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