Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation
Author:
Funder
Natural Science Foundation of Shaanxi Provincial Department of Education
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s11554-023-01371-y.pdf
Reference35 articles.
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2. Fasihi, M.S., Mikhael, W.B.: Overview of current biomedical image segmentation methods. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2016, 803–808. IEEE (2016). https://doi.org/10.1109/CSCI.2016.0156
3. Kucharski, A., Fabijańska, A.: CNN-watershed: a watershed transform with predicted markers for corneal endothelium image segmentation. Biomed. Signal Process. Control 68, 102805 (2021). https://doi.org/10.1016/j.bspc.2021.102805
4. Shang, R., Liu, M., Jiao, L., Feng, J., Li, Y., Stolkin, R.: Region-level SAR image segmentation based on edge feature and label assistance. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2022). https://doi.org/10.1109/TGRS.2022.3217053
5. Kumar, N., Kumar, H.: A fuzzy clustering technique for enhancing the convergence performance by using improved fuzzy C-means and particle swarm optimization algorithms. Data Knowl. Eng. 140, 102050 (2022). https://doi.org/10.1016/j.datak.2022.102050
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