Department of Industrial and Information Engineering and Economics, University of L'Aquila, 67100 L'Aquila, Italy.
Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy.
Sensors (Basel). 2023 Sep 12;23(18):7841. doi: 10.3390/s23187841.
Digital representations of anatomical parts are crucial for various biomedical applications. This paper presents an automatic alignment procedure for creating accurate 3D models of upper limb anatomy using a low-cost handheld 3D scanner. The goal is to overcome the challenges associated with forearm 3D scanning, such as needing multiple views, stability requirements, and optical undercuts. While bulky and expensive multi-camera systems have been used in previous research, this study explores the feasibility of using multiple consumer RGB-D sensors for scanning human anatomies. The proposed scanner comprises three Intel RealSenseTM D415 depth cameras assembled on a lightweight circular jig, enabling simultaneous acquisition from three viewpoints. To achieve automatic alignment, the paper introduces a procedure that extracts common key points between acquisitions deriving from different scanner poses. Relevant hand key points are detected using a neural network, which works on the RGB images captured by the depth cameras. A set of forearm key points is meanwhile identified by processing the acquired data through a specifically developed algorithm that seeks the forearm's skeleton line. The alignment process involves automatic, rough 3D alignment and fine registration using an iterative-closest-point (ICP) algorithm expressly developed for this application. The proposed method was tested on forearm scans and compared the results obtained by a manual coarse alignment followed by an ICP algorithm for fine registration using commercial software. Deviations below 5 mm, with a mean value of 1.5 mm, were found. The obtained results are critically discussed and compared with the available implementations of published methods. The results demonstrate significant improvements to the state of the art and the potential of the proposed approach to accelerate the acquisition process and automatically register point clouds from different scanner poses without the intervention of skilled operators. This study contributes to developing effective upper limb rehabilitation frameworks and personalized biomedical applications by addressing these critical challenges.
数字表示解剖部位对于各种生物医学应用至关重要。本文提出了一种自动对齐程序，用于使用低成本的手持 3D 扫描仪创建上肢解剖结构的精确 3D 模型。目标是克服与前臂 3D 扫描相关的挑战，例如需要多个视图、稳定性要求和光学下切。虽然在以前的研究中使用了庞大而昂贵的多相机系统，但本研究探讨了使用多个消费级 RGB-D 传感器扫描人体解剖结构的可行性。所提出的扫描仪由三个组装在轻量级圆形夹具上的 Intel RealSenseTM D415 深度相机组成，能够从三个视点同时采集。为了实现自动对齐，本文介绍了一种从不同扫描仪姿势采集的提取公共关键点的过程。使用神经网络检测相关手部关键点，该神经网络基于深度相机捕获的 RGB 图像工作。同时，通过处理通过专门开发的算法获取的数据来识别前臂关键点，该算法寻求前臂的骨架线。对齐过程涉及自动、粗糙的 3D 对齐和使用专门为此应用开发的迭代最近点（ICP）算法进行精细注册。该方法在手前臂扫描上进行了测试，并将手动粗对齐后使用商业软件进行 ICP 算法精细注册的结果与手动粗对齐后使用 ICP 算法进行精细注册的结果进行了比较。发现偏差低于 5 毫米，平均值为 1.5 毫米。对获得的结果进行了批判性讨论，并与已发表方法的可用实现进行了比较。结果表明，该方法在提高现有技术水平和加速不同扫描仪姿势的点云自动注册方面具有很大的潜力，而无需熟练操作人员的干预。本研究通过解决这些关键挑战，为开发有效的上肢康复框架和个性化的生物医学应用做出了贡献。
