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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2409.06722 |
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| _version_ | 1866917772753108992 |
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| author | Jiao, Yang Derakhshan, Hananeh Schneider, Barbara St. Pierre Regentova, Emma Yang, Mei |
| author_facet | Jiao, Yang Derakhshan, Hananeh Schneider, Barbara St. Pierre Regentova, Emma Yang, Mei |
| contents | White blood cells (WBCs) are the most diverse cell types observed in the healing process of injured skeletal muscles. In the course of healing, WBCs exhibit dynamic cellular response and undergo multiple protein expression changes. The progress of healing can be analyzed by quantifying the number of WBCs or the amount of specific proteins in light microscopic images obtained at different time points after injury. In this paper, we propose an automated quantifying and analysis framework to analyze WBCs using light microscopic images of uninjured and injured muscles. The proposed framework is based on the Localized Iterative Otsu's threshold method with muscle edge detection and region of interest extraction. Compared with the threshold methods used in ImageJ, the LI Otsu's threshold method has high resistance to background area and achieves better accuracy. The CD68-positive cell results are presented for demonstrating the effectiveness of the proposed work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06722 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle Jiao, Yang Derakhshan, Hananeh Schneider, Barbara St. Pierre Regentova, Emma Yang, Mei Image and Video Processing Computer Vision and Pattern Recognition Machine Learning White blood cells (WBCs) are the most diverse cell types observed in the healing process of injured skeletal muscles. In the course of healing, WBCs exhibit dynamic cellular response and undergo multiple protein expression changes. The progress of healing can be analyzed by quantifying the number of WBCs or the amount of specific proteins in light microscopic images obtained at different time points after injury. In this paper, we propose an automated quantifying and analysis framework to analyze WBCs using light microscopic images of uninjured and injured muscles. The proposed framework is based on the Localized Iterative Otsu's threshold method with muscle edge detection and region of interest extraction. Compared with the threshold methods used in ImageJ, the LI Otsu's threshold method has high resistance to background area and achieves better accuracy. The CD68-positive cell results are presented for demonstrating the effectiveness of the proposed work. |
| title | Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2409.06722 |