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.