Enhancing Image Thresholding with Masi Entropy: An Empirical Approach to Parameter Selection
| dc.contributor.author | Hegazy, Eslam | |
| dc.contributor.author | Gabr, Mohamed | |
| dc.contributor.editor | Skala, Václav | |
| dc.date.accessioned | 2025-07-30T09:41:00Z | |
| dc.date.available | 2025-07-30T09:41:00Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract-translated | Image multilevel thresholding is used as a preprocessing step in computer vision or image processing applications. In recent years, various methods and criteria have been tested to improve thresholding performance and efficiency. State-of-the-art methods combine a metaheuristic optimization algorithm to optimize some objective function. Commonly used objective functions include Otsu criterion and entropy-based methods. Masi entropy is one of the objective functions that recently showed significant potential in image thresholding. It can deal with additivity and non expandability of information. However, in order for applying Masi entropy, there is a parameter r that needs to be tuned. Different works proposed different values and methods to determine the value of r. Nevertheless, there is a lack of validation and comparisons between these methods. This work is an experimental study validating a previously introduced method for selecting r. We compare the performance over two datasets including 700 various images while varying the number of thresholds from 1 to 15. Structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) metrics are used to evaluate the performance. The collective and individual performance improvements are elaborated. It is shown that the tested method achieves a significant improvement in segmentation quality over all tested numbers of thresholds. Higher numbers of thresholds showed greater improvement than smaller numbers. These results demonstrate the practical utility of the tested method in entropy-based image multilevel thresholding. | en |
| dc.format | 8 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | http://www.doi.org/10.24132/CSRN.2025-16 | |
| dc.identifier.issn | 2464-4617 (Print) | |
| dc.identifier.issn | 2464-4625 (online) | |
| dc.identifier.uri | http://hdl.handle.net/11025/62222 | |
| dc.language.iso | en | en |
| dc.publisher | Vaclav Skala - UNION Agency | en |
| dc.rights | © Vaclav Skala - UNION Agency | en |
| dc.rights.access | openAccess | en |
| dc.subject | víceúrovňové prahování | cs |
| dc.subject | segmentace obrazu | cs |
| dc.subject | masi entropie | cs |
| dc.subject | výběr parametrů | cs |
| dc.subject | neřízená klasifikace | cs |
| dc.subject.translated | multilevel thresholding | en |
| dc.subject.translated | image segmentation | en |
| dc.subject.translated | masi entropy | en |
| dc.subject.translated | parameter selection | en |
| dc.subject.translated | unsupervised classification | en |
| dc.title | Enhancing Image Thresholding with Masi Entropy: An Empirical Approach to Parameter Selection | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | conferenceObject | en |
| dc.type.status | Peer reviewed | en |
| dc.type.version | publishedVersion | en |
| local.files.count | 1 | * |
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