V. Bonnet, Takazumi Yamaguchi, A. Dupeyron, S. Andary, Antoine Seilles, P. Fraisse, G. Venture
{"title":"从Kinect传感器自动估计背部解剖标志和3D脊柱曲线","authors":"V. Bonnet, Takazumi Yamaguchi, A. Dupeyron, S. Andary, Antoine Seilles, P. Fraisse, G. Venture","doi":"10.1109/BIOROB.2016.7523746","DOIUrl":null,"url":null,"abstract":"This study aims to develop and evaluate a new method for the automatic extraction and estimate of back anatomical landmark positions and of 3D spine curve from Kinect sensor data. The proposed method allows to robustly reconstruct different indexes of back deformity used in the evaluation of scoliosis. The algorithm input data are the depth map and its corresponding curvature map. From these, regions-of-interest are automatically created and anatomical landmark positions are estimated by finding common patterns between subjects. The results showed that the proposed method can successfully estimate the anatomical landmark positions, as well as the 3D spine curve (average RMS error of 8 mm and 3 mm). The simplicity and generalisation abilities of the proposed method allow to pave the way of future diagnosis solutions for in-home or for small size practice use.","PeriodicalId":235222,"journal":{"name":"2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"16","resultStr":"{\"title\":\"Automatic estimate of back anatomical landmarks and 3D spine curve from a Kinect sensor\",\"authors\":\"V. Bonnet, Takazumi Yamaguchi, A. Dupeyron, S. Andary, Antoine Seilles, P. Fraisse, G. Venture\",\"doi\":\"10.1109/BIOROB.2016.7523746\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aims to develop and evaluate a new method for the automatic extraction and estimate of back anatomical landmark positions and of 3D spine curve from Kinect sensor data. The proposed method allows to robustly reconstruct different indexes of back deformity used in the evaluation of scoliosis. The algorithm input data are the depth map and its corresponding curvature map. From these, regions-of-interest are automatically created and anatomical landmark positions are estimated by finding common patterns between subjects. The results showed that the proposed method can successfully estimate the anatomical landmark positions, as well as the 3D spine curve (average RMS error of 8 mm and 3 mm). The simplicity and generalisation abilities of the proposed method allow to pave the way of future diagnosis solutions for in-home or for small size practice use.\",\"PeriodicalId\":235222,\"journal\":{\"name\":\"2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"16\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BIOROB.2016.7523746\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BIOROB.2016.7523746","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic estimate of back anatomical landmarks and 3D spine curve from a Kinect sensor
This study aims to develop and evaluate a new method for the automatic extraction and estimate of back anatomical landmark positions and of 3D spine curve from Kinect sensor data. The proposed method allows to robustly reconstruct different indexes of back deformity used in the evaluation of scoliosis. The algorithm input data are the depth map and its corresponding curvature map. From these, regions-of-interest are automatically created and anatomical landmark positions are estimated by finding common patterns between subjects. The results showed that the proposed method can successfully estimate the anatomical landmark positions, as well as the 3D spine curve (average RMS error of 8 mm and 3 mm). The simplicity and generalisation abilities of the proposed method allow to pave the way of future diagnosis solutions for in-home or for small size practice use.