Algorithm for segmentation based on an improved three-dimensional Otsu´s thresholding

Considering the misclassification of conventional regional division based on gray level-average gray level-median gray level three-dimensional (3D) histogram, the result of 3D Otsu´s method is not accurate enough. Its computing complexity rapidly increases due to the increment of another eigenvalue. Thus a fast iterative algorithm of the improved 3D Otsu´s method is proposed. Based on the gray level-gradient level-median gray level 3D histogram, the improved regional division and fast iterative formulas of 3D Otsu´s method are derived. The segmentation results, threshold and running time are given in the experimental results and analysis. A comparison is made with the fast iterative algorithms of the traditional 3D Otsu´s method. The experimental results show that the proposed algorithm is more accurate for segmentation, and the detail features are more clearly. The running time reduces approximately 80% because there is no need to ransack the entire solution space while searching the best threshold.