Non-local Three-dimensional Otsu Image Thresholding Segmentation Based on Anisotropic Adaptive Gaussian Weighted Window

Because of the shortage of noise removal and small target preservation for the conventional three- dimensional Otsu (3D-Otsu) method, a new method based on adaptive Gaussian weighted directional window is proposed. The new method improves the window setting method of the 3D-Otsu. The window size, scale and filtering direction are adaptively determined by the local characters. Then, based on the proposed non-local multiple directions similarity measurement, the pattern redundancy in the image can be captured effectively. Finally, the 3D histogram is constructed based on the gray value, weighted mean value and weighted median value, and the threshold vector is computed by the maximum between-class variance method to segment the image. Compared with the commonly-used 2D Otsu method, 2D max-entropy method and 3D-Otsu method, the proposed method has better segmentation performance, with better performance for noise removal and small target preservation.