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Automated VSS-based Burn Scar Assessment using Combined Texture and Color Features of Digital Images in Error-Correcting Output Coding
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery.
Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery.
Linköping University, Department of Clinical and Experimental Medicine, Division of Surgery, Orthopedics and Oncology. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Anaesthetics, Operations and Specialty Surgery Center, Department of Hand and Plastic Surgery.
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, 16744Article in journal (Refereed) Epub ahead of print
Abstract [en]

Assessment of burn scars is an important study in both medical research and clinical settings because it can help determine response to burn treatment and plan optimal surgical procedures. Scar rating has been performed using both subjective observations and objective measuring devices. However, there is still a lack of consensus with respect to the accuracy, reproducibility, and feasibility of the current methods. Computerized scar assessment appears to have potential for meeting such requirements but has been rarely found in literature. In this paper an image analysis and pattern classifcation approach for automating burn scar rating based on the Vancouver Scar Scale (VSS) was developed. Using the image data of pediatric patients, a rating accuracy of 85% was obtained, while 92% and 98% were achieved for the tolerances of one VSS score and two VSS scores, respectively. The experimental results suggest that the proposed approach is very promising as a tool for clinical burn scar assessment that is reproducible and cost-efective.

Place, publisher, year, edition, pages
Nature Publishing Group, 2017. Vol. 7, 16744
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Medical Image Processing Surgery
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URN: urn:nbn:se:liu:diva-143302DOI: 10.1038/s41598-017-16914-0OAI: oai:DiVA.org:liu-143302DiVA: diva2:1162052
Available from: 2017-12-01 Created: 2017-12-01 Last updated: 2017-12-07

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Pham, TuanKarlsson, MatildaAndersson, Caroline M.Mirdell, RobinSjöberg, Folke
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Division of Biomedical EngineeringFaculty of Science & EngineeringDepartment of Hand and Plastic SurgeryDivision of Surgery, Orthopedics and OncologyFaculty of Medicine and Health Sciences
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