The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation DOI Open Access
Kailong Zhang,

Mingyue He,

Lijie Dong

et al.

Published: Aug. 7, 2024

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The parameter q contained this plays an important role various adaptive algorithms and successfully applied bi-level thresholding. In paper, the relationships between pixels’ long-range correlations have further studied within multi-threshold segmentation. It is found that are remarkable stable for images generated by a known physical principle, such as infrared medical CT images. And corresponding can be evaluated using self-adaptive algorithm. results algorithm compared with those Shannon original algorithm, terms quantitative quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) SSIM (Structural Similarity). Furthermore, we observed series same background, values determined consistently kept narrow range. Therefore, similar or identical scenes during imaging would produce strength correlations, which provides potential applications unsupervised processing.

Language: Английский

Multi-threshold image segmentation based on an improved whale optimization algorithm: A case study of Lupus Nephritis DOI
Jinge Shi, Yi Chen, Zhennao Cai

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 96, P. 106492 - 106492

Published: June 7, 2024

Language: Английский

Citations

6
Generalised Entropies for Decision Trees in Classification Under Monotonicity Constraints DOI Open Access

Oumaima Khalaf,

Salvador García, Anis Ben Ishak

et al.

Expert Systems, Journal Year: 2025, Volume and Issue: 42(4)

Published: March 12, 2025

ABSTRACT Several decision‐making approaches involve ordinal labelling between feature values and decision outcomes. These issues refer to classification under monotonicity constraints. Recently, some machine learning have been designed deal with these kinds of problems. Indeed, numerous experiments shown that algorithms are widely used in real‐life applications because their flexibility efficiency terms interpretation predictions. In this paper, we introduce novel for measuring quality information quantity, called Rényi‐Tsallis Monotonic Tree (RTMT), which uses the advantages Rényi Tsallis entropies while incorporating constraints through an optimisation framework. Moreover, Mono‐CART, a variant CART approach adapted monotonic classification. New tree on basis aforementioned considering within system. The conducted using benchmark datasets demonstrate superiority proposed compared existing methods.

Language: Английский

Citations

0
Linearized circular energy curve color image segmentation based on Tsallis entropy DOI
Shaoxun Wang, Jiulun Fan,

Heng Liu

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126746 - 126746

Published: Feb. 1, 2025

Language: Английский

Citations

0
Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques DOI Creative Commons
Zhenjing Xie, Jinran Wu,

Weirui Tang

et al.

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(3), P. e0297284 - e0297284

Published: March 21, 2024

Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes combination accelerating convergence diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination optimal thresholds for final segmentation. The efficacy DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature (FSIM), compared with six contemporary swarm intelligence algorithms. findings reveal that substantially surpasses its counterparts image processing speed. Further empirical analysis dataset comprising cases from level 1 to 5 underscores algorithm’s practical utility, achieving an impressive 80% accuracy identification underscoring potential recognition tasks.

Language: Английский

Citations

1
The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation DOI Creative Commons

Kailong Zhang,

Mingyue He,

Lijie Dong

et al.

Entropy, Journal Year: 2024, Volume and Issue: 26(9), P. 777 - 777

Published: Sept. 10, 2024

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The parameter

Language: Английский

Citations

1
Multi-level Segmentation of Chilli Images Driven by Walrus Optimization Algorithm with Two Strategies DOI
Chen Ye, Peng Shao, Shaoping Zhang

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 371 - 382

Published: Jan. 1, 2024

Language: Английский

Citations

0
Image thresholding method based on Tsallis entropy correlation DOI
Shaoxun Wang, Jiulun Fan

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: May 11, 2024

Language: Английский

Citations

0
The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation DOI Open Access
Kailong Zhang,

Mingyue He,

Lijie Dong

et al.

Published: Aug. 7, 2024

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The parameter q contained this plays an important role various adaptive algorithms and successfully applied bi-level thresholding. In paper, the relationships between pixels’ long-range correlations have further studied within multi-threshold segmentation. It is found that are remarkable stable for images generated by a known physical principle, such as infrared medical CT images. And corresponding can be evaluated using self-adaptive algorithm. results algorithm compared with those Shannon original algorithm, terms quantitative quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) SSIM (Structural Similarity). Furthermore, we observed series same background, values determined consistently kept narrow range. Therefore, similar or identical scenes during imaging would produce strength correlations, which provides potential applications unsupervised processing.

Language: Английский

Citations

0