As the existing Arimoto entropy-based thresholding methods only depend on the probability information from gray histogram and need to search the whole solution space to obtain the optimal threshold with low efficiency,a fast iterative algorithm for threshold segmentation on the basis of two-dimensional Arimoto gray entropy is pro- posed.Firstly,a fast iterative algorithm for threshold selection using one-dimensional Arimoto gray entropy is pro- posed.Secondly,by taking into consideration the gray level uniformity within the object cluster and the back- ground cluster,a two-dimensional Arimoto gray entropy thresholding method on the basis of gray level-average gray level histogram is derived.Then,fast recursive formulae for intermediate variables are given.Finally,a fast ite- rative algorithm is proposed for threshold selection on the basis of two-dimensional Arimoto gray entropy,and the corresponding algorithmic formulae are derived,which helps reduce computation burden greatly.Experimental results show that the proposed algorithm is superior to five existing threshold segmentation algorithms because it runs more rapidly and is more effective in obtaining segmented images with complete objects,clear edges and rich details.