An iterative image segmentation algorithm utilizing spatial information

Abstract

An iterative image segmentation algorithm that segments an image on a pixel-by-pixel basis is described. The observation information to be utilized is the joint gray level values of the pixel to be segmented and those of its neighborhood pixels. The iterative process is initialized by thresholding the image with Otsu's (1979) method. Each pixel is segmented into a class when the a posteriori probability, conditioned on the observation information, that it belongs to this class is a maximum. The newly segmented image is employed to re-estimate the a posteriori probabilities and the segmentation process is repeated until there is no further pixel classification change in a particular run. Among those segmented images generated in the iterative process, the best segmented image is chosen, according to a maximum entropy criterion. Simulation studies demonstrate that the proposed algorithm can achieve very significant improvement in segmentation performance as compared to the more popular thresholds approach. Furthermore, the performance is neither sensitive to the initial threshold value nor the form of the probability density function of the image. Segmentation of practical images also demonstrates that the proposed algorithm is capable of good segmentation results for real-life images.