To further improve the accuracy and speed of image threshold segmentation

an image segmentation method is proposed based on fast iteration for two-dimensional gray entropy threshold selection. First

a fast iterative algorithm for threshold selection that uses one-dimensional gray entropy is proposed. Gray level uniformity within the object cluster and background cluster is then considered

and two-dimensional gray entropy criterion for threshold selection based on gray level-neighborhood average gray level histogram is presented. Finally

a fast iterative algorithm for threshold selection that uses two-dimensional gray entropy is proposed. In addition

recursive algorithms are adopted to calculate the intermediate variables involved in criterion function

thereby avoiding their repetitive computation. Thus

calculating speed is accelerated and calculation amount is greatly reduced. A large number of experimental results show that

compared with three threshold segmentation methods

which have been recently presented

the proposed method has superior image segmentation performance. In the segmented image

object region is complete

edges are clear

and details are rich. Moreover

running time is short and is only approximately 3% of the running time of reciprocal entropy thresholding method with two-dimensional histogram oblique division based on niche chaotic mutation particle swarm optimization. The proposed method has obvious advantages in segmentation results and algorithmic running speed for various gray level images. It is a fast and effective segmentation method that can be used in practical systems.