Abstract: Although the Shannon entropy defined by logarithm is effectively used to measure information uncertain, there exists problem of undefined value and zero value. The computation speed of the existing two-dimensional Shannon cross entropy method can be further improved. Thus, one-dimensional and two-dimensional exponential cross entropy thresholding method is proposed. Firstly, a new definition of the exponential cross entropy is given. One-dimensional exponential cross entropy method for threshold selection is derived. Then, it is extended and two-dimensional exponential cross entropy thresholding method based on decomposition is proposed. The optimal threshold of one-dimensional exponential cross entropy method for pixel grey-level image or neighborhood average grey-level image is computed, respectively. And they are combined to obtain the optimal threshold of two-dimensional exponential cross entropy method. The computation of two-dimensional exponential cross entropy method is converted into two one-dimensional spaces. As a result, the search space is significantly reduced. The computation complexity is reduced from O(L⁴) to O(L). The experimental results show that, compared with the proposed recently two-dimensional Shannon cross entropy method and the two-dimensional Tsallis cross entropy method, the two-dimensional exponential cross entropy thresholding method based on decomposition proposed in this paper can achieve superior segmented results and greatly reduce the running time.