Medical image segmentation is a hotspot in the field of image segmentation, and there are many segmentation methods. As a method of image segmentation, pulse coupled neural network (PCNN) has excellent segmentation effect. Of course, it also reduces the efficiency and effect of segmentation because of the complexity of parameter setting and the need for manual setting. This paper presents a method of searching simplified PCNN parameters by using Harris Hawks optimization (HHO) algorithm. For one thing the number of parameters of PCNN is reduced without affecting the segmentation effect, for another the corresponding parameters of PCNN are searched quickly and accurately by intelligent optimization algorithm. Then, image entropy (H) and mutual information entropy (MI) are introduced as fitness functions. The performance of HHO-PCNN is compared with WOA-PCNN, SCA-PCNN, SSA-PCNN, PSO-PCNN, GWO-PCNN, MVO-PCNN, Otsu and K-means by performance indicators (UM, CM, Precision, Recall, and Dice). The experimental results verify the superiority of this method in image segmentation.
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The authors would like to thank the anonymous reviewers for their constructive comments and suggestions.
This work was supported by the Fundamental Research Funds for the Central Universities(2572019BF04), the Northeast Forestry University Horizontal Project (43217002, 43217005, 43219002).
The authors declare no conflict of interest.
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Jia, H., Peng, X., Kang, L. et al. Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation. Multimed Tools Appl (2020). https://doi.org/10.1007/s11042-020-09228-3
- Image segmentation
- Pulse coupled neural network
- Harris hawks optimization
- Mutual information entropy
- Image entropy