TY - JOUR
T1 - Region extraction method for skin grafts via image analysis
AU - Wada, Daijiro
AU - Kato, Soichiro
AU - Yamaguchi, Yoshihiro
AU - Tanaka, Toshiyuki
N1 - Funding Information:
The authors are grateful for the assistance received from Dr. Yasuhiko Kaita and Dr. Kei Yoshikawa from the Department of Trauma and Critical Care Medicine at the School of Medicine at Kyorin University.
Publisher Copyright:
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - Dermal grafts are used for patients with severe burn injuries whose natural healing abilities are limited or exhausted. This paper proposes a method to segment burnt and grafted areas from images with meshed grafts. First, RGB colour input images are converted to images in the L*a*b* colour space. The transformed images are then segmented with the simple linear iterative clustering superpixel algorithm. Subsequently, the burnt and grafted areas are classified with a support vector machine, and the output area size is modified for all burnt areas. The segmentation results are compared with the areas identified by two dermatologists based on the concordance rate (CR), positive predictive value (PPV), sensitivity, and F-measure values. The proposed segmentation method yielded CRs equal to 84.3 % and 85.9 %, PPVs equal to 87.2 % and 87.9 %, sensitivities equal to 84.7 % and 86.4 %, and F-measure values equal to 85.8 % and 87.0 %. This study developed a segmentation method for classifying burnt and grafted areas from images with meshed grafts. The segmented areas obtained were significantly consistent with those identified by the two dermatologists.
AB - Dermal grafts are used for patients with severe burn injuries whose natural healing abilities are limited or exhausted. This paper proposes a method to segment burnt and grafted areas from images with meshed grafts. First, RGB colour input images are converted to images in the L*a*b* colour space. The transformed images are then segmented with the simple linear iterative clustering superpixel algorithm. Subsequently, the burnt and grafted areas are classified with a support vector machine, and the output area size is modified for all burnt areas. The segmentation results are compared with the areas identified by two dermatologists based on the concordance rate (CR), positive predictive value (PPV), sensitivity, and F-measure values. The proposed segmentation method yielded CRs equal to 84.3 % and 85.9 %, PPVs equal to 87.2 % and 87.9 %, sensitivities equal to 84.7 % and 86.4 %, and F-measure values equal to 85.8 % and 87.0 %. This study developed a segmentation method for classifying burnt and grafted areas from images with meshed grafts. The segmented areas obtained were significantly consistent with those identified by the two dermatologists.
KW - Skin graft
KW - evaluation method
KW - image analysis
KW - super pixel
KW - support vector machine
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U2 - 10.1080/21681163.2021.1966649
DO - 10.1080/21681163.2021.1966649
M3 - Article
AN - SCOPUS:85114415839
SN - 2168-1163
VL - 10
SP - 22
EP - 37
JO - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
JF - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
IS - 1
ER -