【摘要】 目的法向量估计算法是三维可视化中的一个关键环节。常用的法向量算法采用差分法与插值函数相结合计算数据场中任意点的法向量。方法本文介绍的法向量计算方法用二次多项式拟合体数据场 ,采用最小二乘法 ,通过求解线性方程组确定多项式系数 ,进而计算法向量。利用方程系数矩阵的对称性 ,可以简化求解过程。结果通过对各种算法的准确性与处理时间的比较 ,表明该方法能明显提高重建图像的质量同时并没有增加计算复杂度。结论该方法适用于大部分数据场特别是医学图像数据场的法向量估计更多还原

【Abstract】 Objective Normal estimation is the key step for volume visualization. Commonly used methods for normal estimation are based on interpolation and derivative. A novel normal estimation algorithm based on approximation for visualization of medical images was presented in this paper. Method It approximated the density function in local neighborhood with a second degree polynomial function. The coefficients of the polynomial function were solved by minimizing the error of the approximation and the gradient vector at arbitrary point was obtained directly from the analytical derivative of the density function without interpolation. Because of symmetry, the solution of this equation was simplified.This method was tested in several volume data sets. The results and the generation time by different methods were obtained and compared. Result The results showed that this algorithm produced satisfactory quality images while the computational complexity was not increased. Conclusion This approach is preferable for most applications, especially for medical images reconstruction.更多还原