A Pixon-based Image Segmentation Method Considering Textural Characteristics of Image

Abstract

Image segmentation is an essential and critical process in image processing and pattern recognition. In this paper, we proposed a textured-based method to segment an input image into regions. In our developed method, an entropy-based textured map of image is extracted, followed by a histogram equalization step to discriminate different regions. Then with the aim of eliminating unnecessary details and achieving more robustness against unwanted noises, a low-pass filtering technique is successfully used to smooth the image. As the next step, the appropriate pixons are extracted and delivered to a fuzzy c-mean clustering stage to obtain the final image segments. The results of applying the proposed method on several different images indicate its better performance in image segmentation compared to the other pixon-based methods, especially for the images with textured regions.