Open AccessApplication of improved Multiscale normalization
Author(s) -
Shanshan Zhao,
Liangqun Wu,
Tianchi Zhang,
Jing Zhang,
Geng Chen
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/677/4/042083
Subject(s) - normalization (sociology) , weighting , computer science , segmentation , graph , adaptability , k nearest neighbors algorithm , pixel , construct (python library) , image segmentation , artificial intelligence , usability , pattern recognition (psychology) , cut , algorithm , theoretical computer science , medicine , ecology , human–computer interaction , sociology , biology , anthropology , radiology , programming language
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 self-adaptability 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, a method is applicated, 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.