Application of Improved Multi-scale Normalization

In this paper, a method based on neighborhood information is proposed, which adjusts the parameters by using a method of chi square with Gauss weighted distance and the selfadaptability of shared neighbor weighting. And by integrating the nearest neighbor weighted adaptive method, each pixel is automatically given a scale parameter to reduce the need to adjust the parameters. Gauss weighted local neighborhood information is introduced in this paper, to construct the similarity matrix directly on the original image. At the same time, based on the existing graph-cut image segmentation method is necessary to construct a better network graph by using the maximum flow minimum cut. So, NJW algorithm is replaced by a improved Graph cut which combines the multi-scale analysis method, to improve the efficiency of the original segmentation method and enhance the clinical usability.