An improved random walk algorithm based on data-adaptive gaussian smoother for image segmentation

To improve the performance of traditional random walk algorithm, an image segmentation algorithm is proposed, which combined random walk and data-adaptive gaussian smoother. Because the medical or remote sensing images are often occupied by strong noises, a data-adaptive anisotropic filtering technique is proposed to remove noise, The filtering technique built on top of an iterative scheme that can preserve the original significant structures while suppressing the noises to the largest extent, and then compute the gradient image of the filtering image. At last the weights of edges of random walk are determined by both the gray value of original image and the salient features of data-adaptive gaussian smoother. The experimental results from synthetic as well as real images demonstrate that the proposed approach is more effective, accurate and more robust in the noise.