用滤波后轮廓点重建3D模型，提升骨骼结构精度
Filtered 2D Contour-Based Reconstruction of 3D STL Model from CT-DICOM Images
- 对低分辨率图像分割后的2D轮廓点进行滤波去噪
- 通过Delaunay三角剖分逐层构建3D STL模型
- 在骨盆区域重建中显著改善几何形貌
从数字医学成像通信（DICOM）图像中的2D轮廓重构三维立体光刻（STL）模型，对理解解剖结构与畸形至关重要。对计算机断层扫描（CT）图像进行对比度增强、降噪与平滑处理后，采用阈值法进行分割，提取2D轮廓数据点并构建3D STL模型。由于低分辨率图像分割存在误差，原始轮廓点常含离群点，导致三维模型几何失真。本文提出使用滤波后的2D轮廓点重建3D STL模型：对每层图像的轮廓点进行滤波，再通过Delaunay三角剖分逐层拼接，实现模型重构。在基本形状和人体骨盆感兴趣区域（ROI）上验证该方法，结果表明，经滤波处理的2D数据重建的3D模型几何更准确，优于未滤波版本。
原文摘要 · Abstract (English)
Reconstructing a 3D Stereo-lithography (STL) Model from 2D Contours of scanned structure in Digital Imaging and Communication in Medicine (DICOM) images is crucial to understand the geometry and deformity. Computed Tomography (CT) images are processed to enhance the contrast, reduce the noise followed by smoothing. The processed CT images are segmented using thresholding technique. 2D contour data points are extracted from segmented CT images and are used to construct 3D STL Models. The 2D contour data points may contain outliers as a result of segmentation of low resolution images and the geometry of the constructed 3D structure deviate from the actual. To cope with the imperfections in segmentation process, in this work we propose to use filtered 2D contour data points to reconstruct 3D STL Model. The filtered 2D contour points of each image are delaunay triangulated and joined layer-by-layer to reconstruct the 3D STL model. The 3D STL Model reconstruction is verified on i) 2D Data points of basic shapes and ii) Region of Interest (ROI) of human pelvic bone and are presented as case studies. The 3D STL model constructed from 2D contour data points of ROI of segmented pelvic bone with and without filtering are presented. The 3D STL model reconstructed from filtered 2D data points improved the geometry of model compared to the model reconstructed without filtering 2D data points.
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