As a generalization of one-dimensional minimum-error threshold segmentation method was widely used in image segmentation.In this paper,two-dimensional minimum-error threshold algorithm and its fast algorithm(here referring to the original method) are improved,and threshold acquirement formula of the original minimum-error threshold segmentation method was deformed.By using look-up table method,acquirement of two-dimensional threshold is transformed into that of two one-dimensional threshold,so the calculation is simplified and time complexity is reduced from O(L2) to O(L).Experimental results show this decomposition algorithm outperforms original algorithm.Without losing the robustness to noise and needing more time and space,it can get the same segmentation threshold as the original method.

As the classical image segmentation algorithm, Ostu threshold algorithm has been widely used in image processing. The using of the algorithm which was developed on the basis of two-dimensional, high-dimensional threshold algorithm was restricted because of its complexity. But they consider both the gray value of a pixel and the average gray value of its neighborhood, thus is more robust to noise. By constructing look-up table recursively, its fast algorithm reduces its complexity from four-dimensional to two-dimensional. Algorithm based on the decomposition threshold selection, using threshold of two one-dimensional Ostu algorithms to replace it of two-dimensional Ostu, the method is the same as two-dimensional Ostu algorithm in segmentation threshold, and the algorithm's time complexity can be reduced to one-dimensional. In the paper, the algorithm was improved, one-dimensional threshold average decomposition for two-dimensional Ostu algorithm was proposed. It can be confirmed by a large of number experiments, The algorithm not only maintained advantages of less time and smaller space of calculating the threshold but also better segmentation results can be found from experiments.

As a classical image segmentation method, Ostu threshold algorithm had been applied widely in image processing. This paper had a comparison of the following two-dimensional Ostu thresholding method. One-dimensional Ostu method considered only grayscale information of the pixel, 2D Ostu algorithm considered both the gray value of a pixel and the average gray value of its neighborhood, thus is more robust to noise. But because calculating of the two-dimensional Ostu threshold method demands a long time, so restricted its use, by constructing look-up tables recursively, its fast algorithm reduces its complexity from O(L4) to O(L2), based on the decomposition of 2D Ostu adaptive algorithm. When the hypothesis of original 2D Ostu algorithm holds, the method can get the same segmentation threshold as the original two-dimensional method, while the computational complexity is reduced further. In the paper, the algorithm is improved on the basis of the original one, one-dimensional threshold average decomposition of two-dimensional Ostu algorithm is proposed. The algorithm not only maintained advantages of less time and smaller space of calculating the threshold of 2D Ostu adaptive algorithm, of the threshold shorter and space advantages of a smaller, but also better segmentation results can be found to get from experiment.