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https://hdl.handle.net/10356/184354
Title: | Quantification of muscle fiber malformations using edge detection to investigate chronic muscle pressure ulcers | Authors: | Ong, Charlene Zhi Lin Nasir, N. Jannah M. Welsch, Roy E. Tucker-Kellogg, Lisa Rajapakse, Jagath Chandana |
Keywords: | Computer and Information Science | Issue Date: | 2024 | Source: | Ong, C. Z. L., Nasir, N. J. M., Welsch, R. E., Tucker-Kellogg, L. & Rajapakse, J. C. (2024). Quantification of muscle fiber malformations using edge detection to investigate chronic muscle pressure ulcers. Frontiers in Bioinformatics, 4, 1450146-. https://dx.doi.org/10.3389/fbinf.2024.1450146 | Journal: | Frontiers in Bioinformatics | Abstract: | Background: Microscopy of regenerated tissue shows different morphologies between the healing of acute wounds and chronic wounds. This difference can be seen manually by biologists, but computational methods are needed to automate the characterization of morphology and regenerative quality in regenerated muscle tissue. Results: From the detected edge segments, we computed several imaging biomarkers of interest, such as median tortuosity, number of edge segments normalized by area, median edge segment distance and interquartile range of orientation angles of edge segments of the microscope images of successful and unsuccessful muscle regeneration. We observed that muscle fibers in saline-treated pressure ulcers had a larger interquartile range of orientation angles of the edge segments (p = 0.05) and shorter edge segment distances (p = 0.003) compared to those of acute cardiotoxin injuries. Conclusion: Our edge detection method was able to identify statistically significant differences in some of the imaging biomarkers between saline-treated pressure ulcers and cardiotoxin injuries, suggesting that chronic pressure ulcers have increased muscle fiber malformations compared to cardiotoxin injuries. | URI: | https://hdl.handle.net/10356/184354 | ISSN: | 2673-7647 | DOI: | 10.3389/fbinf.2024.1450146 | Schools: | College of Computing and Data Science | Research Centres: | Health Informatics Lab | Rights: | © 2024 Ong, Nasir, Welsch, Tucker-Kellogg and Rajapakse. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | CCDS Journal Articles |
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