Version 1
: Received: 6 August 2024 / Approved: 7 August 2024 / Online: 7 August 2024 (14:19:06 CEST)
How to cite:
Zhang, K.; He, M.; Dong, L.; Ou, C. The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation. Preprints2024, 2024080511. https://doi.org/10.20944/preprints202408.0511.v1
Zhang, K.; He, M.; Dong, L.; Ou, C. The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation. Preprints 2024, 2024080511. https://doi.org/10.20944/preprints202408.0511.v1
Zhang, K.; He, M.; Dong, L.; Ou, C. The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation. Preprints2024, 2024080511. https://doi.org/10.20944/preprints202408.0511.v1
APA Style
Zhang, K., He, M., Dong, L., & Ou, C. (2024). The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation. Preprints. https://doi.org/10.20944/preprints202408.0511.v1
Chicago/Turabian Style
Zhang, K., Lijie Dong and Congjie Ou. 2024 "The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation" Preprints. https://doi.org/10.20944/preprints202408.0511.v1
Abstract
Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels’ long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels’ correlations are remarkable and stable for images generated by a known physical principle, such as infrared images and medical CT images. And the corresponding non-extensive parameter q can be evaluated by using self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of Shannon entropy algorithm and the original Tsallis entropy algorithm, in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
