计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 308-313.doi: 10.11896/jsjkx.201100044

• 图像处理& 多媒体技术 • 上一篇    下一篇

基于三维图像的疤痕面积计算

姚楠, 张征   

  1. 华中科技大学人工智能与自动化学院 武汉430074
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 张征(leaf@mail.hust.edu.cn)
  • 作者简介:m201872787@hust.edu.cn

Scar Area Calculation Based on 3D Image

YAO Nan, ZHANG Zheng   

  1. School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:YAO Nan,born in 1997,postgraduate.Her main research interests include segmentation of 3D images and so on.
    ZHANG Zheng,born in 1976,Ph.D,associate professor.His main research interests include automation theory teaching and application,smart grid,water network and other Internet of things research work.

摘要: 目前法医鉴定受伤疤痕面积主要采用人工的方式,其存在一定的不稳定性和时耗问题。因此,提出了基于三维图像的疤痕面积计算的法医鉴定方法。首先使用三维激光扫描仪获取待鉴定皮肤的三维图像数据;其次对数据进行预处理,除去背景环境部分以及噪点,同时通过下采样调整点云分辨率;然后使用颜色区域生长方法,对伤疤进行自动区域分割,并辅以人工交互以调整目标疤痕区域;最后利用曲面重建后的目标区域来计算疤痕面积。实验结果表明,所提方法与当前法医数字化处理方法相比,误差保持在5%以内,耗时减少了20%以上。

关键词: 点云分割, 法医鉴定, 面积计算, 曲面重建, 三维图像处理

Abstract: At present,forensic medicine mainly uses artificial methods to identify the area of injured scars,which has some instability and time-consuming problems.Therefore,a forensic identification method based on the 3D image to calculate the scar area is proposed.Firstly,a 3D laser scanner is used to obtain 3D image data of the unidentified scar.Secondly,the data is preprocessed to remove the background and noise,and the point cloud resolution is adjusted by down-sampling.Then the color area growth method is used to perform automatic region segmentation,also manual interaction is supplemented to adjust the target scar area.Finally,after surface reconstruction,the target scar is used to calculate its area.The results show that,compared with the current digital processing method of forensic medicine,the error is kept within 5% and the time consumption is reduced by more than 20%.

Key words: 3D image processing, Area calculation, Forensic identification, Point cloud segmentation, Surface reconstruction

中图分类号: 

  • TP311.5
[1]QI L,LI P,JIN B,et al.Application of three-dimensional meas-urement technology in identification of human injury degree[J].Chinese Journal of Forensic Medicine,2019,34 (2):165-167,164.
[2]LASSCHUIT J W J,FEATHERSTON J,TONKS K T T.Reliability of a Three-Dimensional Wound Camera and Correlation With Routine Ruler Measurement in Diabetes-Related Foot Ulceration[J/OL].Journal of Diabetes Science and Technology,2020. https://www.researchgate.net/publication/347201083_Reliability_of_a_Three-Dimensional_Wound_Camera_and_Correlation_With_Routine_Ruler_Measurement_in_Diabetes-Related_Foot_Ulceration.
[3]TAN L.Automatic Assessment of Burn AreaBased on 3D Human Surface Reconstruction Technology[J].Shanghai:Donghua University,2014.
[4]SHENG W,ZENG D,WAN Y,et al.BurnCalc assessment study of computer-aided individual three-dimensional burn area calculation[J].Journal of Translational Medicine,2014,12(1):242.
[5]LU J,WANG L,ZHANG Y C,TANG H T,XIA Z F.Clinical application effect of burncalc 3D scanning system in burn wound area evaluation[J].Chinese Journal of Burns,2017,33 (10):597-601.
[6]YAO L,DONG G S,TANG H T,et al.The application of athree-dimensional human body surface imaging technique in the estimation of human burn area[J].Journal of Donghua University:Natural Science Edition,2015,41(1):84-90.
[7]YANG H J.Application of multi-slice spiral CT three-dimensional reconstruction in diagnosis of rib fracture and forensic clinical identification[J].Legal system Expo,2020 (8):155-156.
[8]FU J Q.Preliminary study on three-dimensional measurement of injured scar surface with structured light[D].Huazhong:Huazhong University of Science and Technology,2016.
[9]NIU C G,LIU Y J,LI Z M,et al.Three-dimensional target recognition and modelsegmentation method based on point cloud data[J].Journal of Graphics,2019,40(2):274-281.
[10]QI C R,SU H,MO K,et al.Pointnet:Deep learning on point sets for 3d classification and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:652-660.
[11]QI C R,YI L,SU H,et al.Pointnet++:Deep hierarchical feature learning on point sets in a metric space[J].Advances in Neural Information Processing Systems,2017,30:5099-5108.
[12]ZABIHOLLAHY F,WHITE J A,UKWATTA E.Convolutional neural network-based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement MR images[J].Medical Physics,2019,46(4):1740-1751.
[13]LASSCHUIT J W J,FEATHERSTON J,TONKS K T T.Reliability of a Three-Dimensional Wound Camera and Correlation With Routine Ruler Measurement in Diabetes-Related Foot Ulceration[J].Journal of Diabetes Science and Technology,2020:193229682097465.
[14]TONG J.Scanning and reconstruction of 3D object and human body based on depth camera[J].Zhejiang University,2012.
[15]HU P P.Development of 3-d scanning system based on RGBD depth camera and research on multi-suit Simulation Algorithm[J].Donghua University,2017.
[16]GUO H,SU W,ZHU D H,et al.Point cloud library PCL from entry to proficiency[M].Machinery Industry Press:Xi'an,2019:100-120.
[17]KAZHDAN M,BOLITHO M,HOPPE H.Poisson surface reconstruction[C]//Proceedings of the Fourth Eurographics Symposium on Geometry Processing.2006,7.
[18]HUANG P,ZHENG Q,LIANG C.Overview of image segmentation methods[J].Journal of Wuhan University (Science Edition),2020,66(6):519-531.
[19]ALHARAN A F H,FATLAWI H K,ALI N S.A cluster-based feature selection method for image texture classification[J].Indonesian Journal of Electrical Engineering and Computer Ence,2019,14(3):1433-1442.
[20]YAO L,CHENG Y R,WU H.Three-dimensional area measurement based on grid model[J].Software Guide,2016,15(2):98-101.
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[2] 徐利敏,吴刚.
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[3] 秦绪佳,陈楼衡,谭小俊,郑红波,张美玉.
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[4] 邱春丽,许宏丽.
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