A regularized feature extraction algorithm based on the improved Canny algorithm is proposed to address the problems related to applying a feature extraction algorithm to scattered point clouds, i.e., computational burden and lack of regularization. First, scattered point clouds are resampled according to different range resolutions, and the point clouds are rasterized regularly. Second, the gray value of the grid matrix is assigned by the optimized substitution method, and the scattered point clouds are projected into two-dimensional images. Finally, the improved Canny algorithm is used to extract feature regularization from the two-dimensional images. Comparative experimental results demonstrate that the proposed method yields less noise, has strong maneuverability, and can extract features from a straight line boundary or complex curve boundary efficiently. The proposed method will play an important role in the registration of point clouds and images, as well as three-dimensional reconstruction.