Image segmentation by improved minimum spanning tree with fractional differential and Canny detector

Abstract

In this study, we propose an algorithm that uses an improved Minimum Spanning Tree algorithm and a modified Canny edge detector to segment images that contain a considerable amount of noises. First, we use our modified Canny operator to pre-process an image, and record the obtained object boundary information; then, we apply the improved Minimum Spanning Tree algorithm to associate the above information with boundary points in order to separate edges into two classes in the image, namely the inner and boundary regions. In particular, Minimum Spanning Tree algorithm is improved by using Fractional differential and combining the functions of the intra-regional and inter-regional differences with a function for edge weights. Based on the experimental results, compared with the other four exiting algorithms, the new algorithm has the higher accuracy and the better effect for noised image segmentation.