Yao W, Abolmaesumi P, Greenspan M, Ellis R E
School of Computing, Queen's University, Kingston, ON, Canada.
IEEE Trans Med Imaging. 2005 Aug;24(8):997-1010. doi: 10.1109/TMI.2005.850541.
The normal direction of the bone contour in computed tomography (CT) images provides important anatomical information and can guide segmentation algorithms. Since various bones in CT images have different sizes, and the intensity values of bone pixels are generally nonuniform and noisy, estimation of the normal direction using a single scale is not reliable. We propose a multiscale approach to estimate the normal direction of bone edges. The reliability of the estimation is calculated from the estimated results and, after re-scaling, the reliability is used to further correct the normal direction. The optimal scale at each point is obtained while estimating the normal direction; this scale is then used in a simple edge detector. Our experimental results have shown that use of this estimated/corrected normal direction improves the segmentation quality by decreasing the number of unexpected edges and discontinuities (gaps) of real contours. The corrected normal direction could also be used in postprocessing to delete false edges. Our segmentation algorithm is automatic, and its performance is evaluated on CT images of the human pelvis, leg, and wrist.
计算机断层扫描（CT）图像中骨轮廓的法线方向提供了重要的解剖学信息，并可指导分割算法。由于CT图像中的各种骨骼大小不同，且骨像素的强度值通常不均匀且有噪声，因此使用单一尺度估计法线方向并不可靠。我们提出了一种多尺度方法来估计骨边缘的法线方向。根据估计结果计算估计的可靠性，重新缩放后，该可靠性用于进一步校正法线方向。在估计法线方向时获得每个点的最佳尺度；然后将此尺度用于简单的边缘检测器。我们的实验结果表明，使用这种估计/校正后的法线方向可通过减少真实轮廓中意外边缘和不连续性（间隙）的数量来提高分割质量。校正后的法线方向还可用于后处理以删除虚假边缘。我们的分割算法是自动的，并在人体骨盆、腿部和腕部的CT图像上评估其性能。
