• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于运动结构的红外传感器和辐射处理的解剖 3D 建模:迈向糖尿病足诊断工具。

Anatomical 3D Modeling Using IR Sensors and Radiometric Processing Based on Structure from Motion: Towards a Tool for the Diabetic Foot Diagnosis.

机构信息

Departamento de Ingeniería Eléctrica/Sección de Bioelectrónica, Centro de Investigación y de Estudios Avanzados del IPN, Av. Instituto Politécnico Nacional 2508, Col. San Pedro Zacatenco, Gustavo A. Madero, Ciudad de México 07360, Mexico.

Centre de Recherche en Automatique de Nancy (CRAN)/CNRS, Université de Lorraine, 2 Avenue de la Forêt de Haye, 54516 Vandœuvre-Lès-Nancy, Lorraine, France.

出版信息

Sensors (Basel). 2021 Jun 6;21(11):3918. doi: 10.3390/s21113918.

DOI:10.3390/s21113918
PMID:34204151
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8201207/
Abstract

Medical infrared thermography has proven to be a complementary procedure to physiological disorders, such as the diabetic foot. However, the technique remains essentially based on 2D images that display partial anatomy. In this context, a 3D thermal model provides improved visualization and faster inspection. This paper presents a 3D reconstruction method associated with temperature information. The proposed solution is based on a Structure from Motion and Multi-view Stereo approach, exploiting a set of multimodal merged images. The infrared images were obtained by automatically processing the radiometric data to remove thermal interferences, segment the RoI, enhance false-color contrast, and for multimodal co-registration under a controlled environment and a ∆T < 2.6% between the RoI and thermal interferences. The geometric verification accuracy was 77% ± 2%. Moreover, a normalized error was adjusted per sample based on a linear model to compensate for the curvature emissivity (error ≈ 10% near to 90°). The 3D models were displayed with temperature information and interaction controls to observe any point of view. The temperature sidebar values were assigned with information retrieved only from the RoI. The results have proven the feasibility of the 3D multimodal construction to be used as a promising tool in the diagnosis of diabetic foot.

摘要

医学红外热成像已被证明是一种对生理失调(如糖尿病足)的补充性检测方法。然而,该技术主要基于显示部分解剖结构的 2D 图像。在这种情况下,3D 热模型可提供更好的可视化和更快的检查。本文提出了一种与温度信息相关的 3D 重建方法。该解决方案基于运动结构和多视图立体视觉方法,利用一组多模态合并图像。红外图像通过自动处理辐射测量数据来获得,以消除热干扰、分割 ROI、增强假彩色对比度,并在受控环境下对多模态进行配准,ROI 和热干扰之间的温差<2.6%。几何验证精度为 77%±2%。此外,还根据线性模型针对每个样本调整了归一化误差,以补偿曲率发射率(在接近 90°的位置误差约为 10%)。3D 模型通过温度信息和交互控制进行显示,以便观察任何视角。温度侧边栏值仅从 ROI 中检索到的信息中分配。结果证明了 3D 多模态构建作为糖尿病足诊断的有前途工具的可行性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/d256c376f027/sensors-21-03918-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/bd1cf8f280e6/sensors-21-03918-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/de607983c62b/sensors-21-03918-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/3ecea50443b9/sensors-21-03918-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/ad5f78761baa/sensors-21-03918-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/79847bf5da9d/sensors-21-03918-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/fcf0b5df08c4/sensors-21-03918-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/1648d8f5b91f/sensors-21-03918-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/1f0f8c7dbd5e/sensors-21-03918-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/f8a20886752c/sensors-21-03918-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/e2fc3750692e/sensors-21-03918-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/3f1abd48d3c6/sensors-21-03918-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/9e13f0d0bf37/sensors-21-03918-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/7ca10ff6e811/sensors-21-03918-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/db52fe0261f9/sensors-21-03918-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/776815d6efcf/sensors-21-03918-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/7b5c93dcaa36/sensors-21-03918-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/9587d677f48b/sensors-21-03918-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/afe88678d837/sensors-21-03918-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/2eb45a205565/sensors-21-03918-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/a43699c4a74b/sensors-21-03918-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/a31847e55649/sensors-21-03918-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/d256c376f027/sensors-21-03918-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/bd1cf8f280e6/sensors-21-03918-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/de607983c62b/sensors-21-03918-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/3ecea50443b9/sensors-21-03918-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/ad5f78761baa/sensors-21-03918-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/79847bf5da9d/sensors-21-03918-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/fcf0b5df08c4/sensors-21-03918-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/1648d8f5b91f/sensors-21-03918-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/1f0f8c7dbd5e/sensors-21-03918-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/f8a20886752c/sensors-21-03918-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/e2fc3750692e/sensors-21-03918-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/3f1abd48d3c6/sensors-21-03918-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/9e13f0d0bf37/sensors-21-03918-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/7ca10ff6e811/sensors-21-03918-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/db52fe0261f9/sensors-21-03918-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/776815d6efcf/sensors-21-03918-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/7b5c93dcaa36/sensors-21-03918-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/9587d677f48b/sensors-21-03918-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/afe88678d837/sensors-21-03918-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/2eb45a205565/sensors-21-03918-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/a43699c4a74b/sensors-21-03918-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/a31847e55649/sensors-21-03918-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/375b/8201207/d256c376f027/sensors-21-03918-g022.jpg

相似文献

1
Anatomical 3D Modeling Using IR Sensors and Radiometric Processing Based on Structure from Motion: Towards a Tool for the Diabetic Foot Diagnosis.基于运动结构的红外传感器和辐射处理的解剖 3D 建模:迈向糖尿病足诊断工具。
Sensors (Basel). 2021 Jun 6;21(11):3918. doi: 10.3390/s21113918.
2
Infrared 3D Thermography for Inflammation Detection in Diabetic Foot Disease: A Proof of Concept.红外 3D 热成像在糖尿病足病炎症检测中的应用:概念验证。
J Diabetes Sci Technol. 2020 Jan;14(1):46-54. doi: 10.1177/1932296819854062. Epub 2019 Jun 14.
3
3D Multi-Modality Medical Imaging: Combining Anatomical and Infrared Thermal Images for 3D Reconstruction.3D 多模态医学成像:结合解剖和红外热图像进行 3D 重建。
Sensors (Basel). 2023 Feb 1;23(3):1610. doi: 10.3390/s23031610.
4
An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images.临床前研究中的解剖热三维模型:结合 CT 和热图像。
Sensors (Basel). 2021 Feb 9;21(4):1200. doi: 10.3390/s21041200.
5
Diabetic Plantar Foot Segmentation in Active Thermography Using a Two-Stage Adaptive Gamma Transform and a Deep Neural Network.基于两阶段自适应伽马变换和深度神经网络的主动热成像糖尿病足底分割。
Sensors (Basel). 2023 Oct 17;23(20):8511. doi: 10.3390/s23208511.
6
Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring.糖尿病足监测中热红外与可见光图像配准方法评估。
Sensors (Basel). 2021 Mar 24;21(7):2264. doi: 10.3390/s21072264.
7
Comparative thermal map of the foot between patients with and without diabetes through the use of infrared thermography.通过红外热成像技术对糖尿病患者与非糖尿病患者足部的对比热图。
Enferm Clin (Engl Ed). 2020 Mar-Apr;30(2):119-123. doi: 10.1016/j.enfcli.2018.11.002. Epub 2019 Jan 7.
8
Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study.基于智能手机热像仪的糖尿病足溃疡移动检测系统:一项可行性研究。
Biomed Eng Online. 2017 Oct 3;16(1):117. doi: 10.1186/s12938-017-0408-x.
9
3D thermal medical image visualization tool: Integration between MRI and thermographic images.3D 热医学图像可视化工具:MRI 与热成像图像之间的整合
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:5583-6. doi: 10.1109/EMBC.2014.6944892.
10
Update on the Use of Infrared Thermography in the Early Detection of Diabetic Foot Complications: A Bibliographic Review.红外热成像技术在糖尿病足并发症早期检测中的应用进展:文献综述。
Sensors (Basel). 2023 Dec 31;24(1):252. doi: 10.3390/s24010252.

引用本文的文献

1
A Projective-Geometry-Aware Network for 3D Vertebra Localization in Calibrated Biplanar X-Ray Images.用于校准双平面X射线图像中三维椎体定位的射影几何感知网络。
Sensors (Basel). 2025 Feb 13;25(4):1123. doi: 10.3390/s25041123.
2
3D Multi-Modality Medical Imaging: Combining Anatomical and Infrared Thermal Images for 3D Reconstruction.3D 多模态医学成像:结合解剖和红外热图像进行 3D 重建。
Sensors (Basel). 2023 Feb 1;23(3):1610. doi: 10.3390/s23031610.

本文引用的文献

1
Assessment of Registration Methods for Thermal Infrared and Visible Images for Diabetic Foot Monitoring.糖尿病足监测中热红外与可见光图像配准方法评估。
Sensors (Basel). 2021 Mar 24;21(7):2264. doi: 10.3390/s21072264.
2
Medical applications of infrared thermography: A review.红外热成像技术的医学应用:综述
Infrared Phys Technol. 2012 Jul;55(4):221-235. doi: 10.1016/j.infrared.2012.03.007. Epub 2012 Apr 13.
3
Recognition of ischaemia and infection in diabetic foot ulcers: Dataset and techniques.糖尿病足溃疡中缺血和感染的识别:数据集与技术
Comput Biol Med. 2020 Feb;117:103616. doi: 10.1016/j.compbiomed.2020.103616. Epub 2020 Jan 10.
4
Infrared 3D Thermography for Inflammation Detection in Diabetic Foot Disease: A Proof of Concept.红外 3D 热成像在糖尿病足病炎症检测中的应用:概念验证。
J Diabetes Sci Technol. 2020 Jan;14(1):46-54. doi: 10.1177/1932296819854062. Epub 2019 Jun 14.
5
An Application for Skin Macules Characterization Based on a 3-Stage Image-Processing Algorithm for Patients with Diabetes.基于糖尿病患者图像处理算法的皮肤斑特征应用。
J Healthc Eng. 2018 Dec 16;2018:9397105. doi: 10.1155/2018/9397105. eCollection 2018.
6
Medical Imaging and Laboratory Analysis of Diagnostic Accuracy in 107 Consecutive Hospitalized Patients With Diabetic Foot Osteomyelitis and Partial Foot Amputations.107例糖尿病足骨髓炎并部分足截肢连续住院患者诊断准确性的医学影像与实验室分析
Foot Ankle Spec. 2018 Oct;11(5):433-443. doi: 10.1177/1938640017750255. Epub 2017 Dec 31.
7
Five-Year Cost-effectiveness of the Multidisciplinary Risk Assessment and Management Programme-Diabetes Mellitus (RAMP-DM).多学科风险评估和管理计划-糖尿病(RAMP-DM)的五年成本效益。
Diabetes Care. 2018 Feb;41(2):250-257. doi: 10.2337/dc17-1149. Epub 2017 Dec 15.
8
Effect of an intensified multifactorial intervention on cardiovascular outcomes and mortality in type 2 diabetes (J-DOIT3): an open-label, randomised controlled trial.强化多因素干预对 2 型糖尿病患者心血管结局和死亡的影响(J-DOIT3):一项开放标签、随机对照试验。
Lancet Diabetes Endocrinol. 2017 Dec;5(12):951-964. doi: 10.1016/S2213-8587(17)30327-3. Epub 2017 Oct 24.
9
An explorative study on the validity of various definitions of a 2·2°C temperature threshold as warning signal for impending diabetic foot ulceration.探索性研究各种 2.2°C 温度阈值定义作为即将发生的糖尿病足溃疡预警信号的有效性。
Int Wound J. 2017 Dec;14(6):1346-1351. doi: 10.1111/iwj.12811. Epub 2017 Oct 9.
10
Medical Imaging in Differentiating the Diabetic Charcot Foot from Osteomyelitis.医学影像在鉴别糖尿病夏科氏足与骨髓炎中的应用
Clin Podiatr Med Surg. 2017 Jan;34(1):9-14. doi: 10.1016/j.cpm.2016.07.002. Epub 2016 Sep 3.