PDF(Pubmed)
Abstract：
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.
摘要：
传统的三维重建方法主要使用图像处理技术或深度学习分割模型进行肋骨提取。后处理后，实现了基于体素的肋骨重建。然而,这些方法的重建精度有限，计算效率低。为了克服这些限制,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，我们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来实现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，通过所提出的方法生成的3D肋骨模型在重建精度方面实现了40％的提高，并且效率是前者的两倍。
