A Robust Thresholding Algorithm Framework based on Reconstruction and Dimensionality Reduction of the Three Dimensional Histogram

In this work, a robust thresholding algorithm framework based on reconstruction and dimensionality reduction of the three-dimensional (3-D) histogram is proposed with the consideration of the poor anti-noise performance in existing 3-D histogram-based segmentation methods due to the obviously wrong region division. Firstly, our method reconstructs the 3-D histogram based on the distribution of noisy points which reduce its segmentation performance. Secondly, we transfer the region division in 3D histogram from eight partitions into two parts, thus reducing the searching space of threshold from 3-dimension to 1-dimension, which saves a lot of processing time and memory space. Thirdly, we apply the presented framework to global thresholding algorithms such as Otsu method, minimum error method, and maximum entropy method and so on, and propose corresponding robust global thresholding algorithms. Finally, segmentation result and running time are given at the end of this paper compared with those of 3-D Otsu’s method, Otsu method, minimum error method and maximum entropy method. The experimental results show that the presented method has better anti-noise performance and visual quality compared with the above four approaches, and has lower time complexity than 3-D Otsu’s method.