{"title":"A Quick Means for the Burnt Skin Area Calculation via Multiple-view Structured Light Sensors","authors":"Di Wu, Yuping Ye, Feifei Gu, Zhan Song","doi":"10.1109/ROBIO58561.2023.10354748","DOIUrl":null,"url":null,"abstract":"With the fast development of computer vision and artificial intelligence, many technologies from these fields have been introduced to the medical domain. Accurate estimation of burnt skin area is crucial for treatment plan selection and prognostic decision-making. However, state-of-art estimation of burnt skin area exhibits inadequate accuracy and acquisition efficiency. In this paper, a burnt skin acquisition system based on the infrared structured light 3D imaging method is developed. To accurately segment the burnt skin point cloud from the raw point cloud acquired by the proposed system, we employ the Segment Anything Model (SAM). Subsequently, the point clouds segmented from different views are registered using pre-calibrated parameters. Moreover, the surface reconstruction algorithm is employed to generate triangular meshes. Finally, we calculate the area of all the triangular mesh facets to represent the area of burnt skin. Several experiments were conducted to demonstrate the accuracy of the proposed method.","PeriodicalId":505134,"journal":{"name":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","volume":"76 5","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROBIO58561.2023.10354748","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With the fast development of computer vision and artificial intelligence, many technologies from these fields have been introduced to the medical domain. Accurate estimation of burnt skin area is crucial for treatment plan selection and prognostic decision-making. However, state-of-art estimation of burnt skin area exhibits inadequate accuracy and acquisition efficiency. In this paper, a burnt skin acquisition system based on the infrared structured light 3D imaging method is developed. To accurately segment the burnt skin point cloud from the raw point cloud acquired by the proposed system, we employ the Segment Anything Model (SAM). Subsequently, the point clouds segmented from different views are registered using pre-calibrated parameters. Moreover, the surface reconstruction algorithm is employed to generate triangular meshes. Finally, we calculate the area of all the triangular mesh facets to represent the area of burnt skin. Several experiments were conducted to demonstrate the accuracy of the proposed method.