Zhu Darong, Wang Diao, Chen Yuanjiao, Xu Zhe, He Bishi
School of Automation (School of Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China.
Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou 310024, China.
Sensors (Basel). 2024 Jun 23;24(13):4076. doi: 10.3390/s24134076.
The traditional methods for 3D reconstruction mainly involve using image processing techniques or deep learning segmentation models for rib extraction. After post-processing, voxel-based rib reconstruction is achieved. However, these methods suffer from limited reconstruction accuracy and low computational efficiency. To overcome these limitations, this paper proposes a 3D rib reconstruction method based on point cloud adaptive smoothing and denoising. We converted voxel data from CT images to multi-attribute point cloud data. Then, we applied point cloud adaptive smoothing and denoising methods to eliminate noise and non-rib points in the point cloud. Additionally, efficient 3D reconstruction and post-processing techniques were employed to achieve high-accuracy and comprehensive 3D rib reconstruction results. Experimental calculations demonstrated that compared to voxel-based 3D rib reconstruction methods, the 3D rib models generated by the proposed method achieved a 40% improvement in reconstruction accuracy and were twice as efficient as the former.
传统的三维重建方法主要涉及使用图像处理技术或深度学习分割模型来提取肋骨。经过后处理，实现基于体素的肋骨重建。然而，这些方法存在重建精度有限和计算效率低的问题。为了克服这些限制，本文提出了一种基于点云自适应平滑和去噪的三维肋骨重建方法。我们将CT图像中的体素数据转换为多属性点云数据。然后，应用点云自适应平滑和去噪方法来消除点云中的噪声和非肋骨点。此外，采用高效的三维重建和后处理技术，以获得高精度和全面的三维肋骨重建结果。实验计算表明，与基于体素的三维肋骨重建方法相比，本文提出的方法生成的三维肋骨模型在重建精度上提高了40%，并且效率是前者的两倍。
