Research On The Algorithm Of Vese-Chan Model For Multiphase Image Segmentation

Multiphase image segmentation is an important technology of image processing and analysis,which has widely found a lot of applications including multi-target detection and recognition,3D image segmentation and reconstruction.The aim of multiphase image segmentation is to partition images into different regions without any overlaps and vacuum according to different image features automatically.It is a natural extension of the twophase image segmentation based on the variational image analysis framework.Multiphase image segmentation Vese-Chan model uses less label functions to construct the characteristic function for region division,which has the advantage of small scale.The main work includes:1.A vectorized continuous max-flow(VCMF)method of multiphase image segmentation Vese-Chan mode is proposed.Two kinds of characteristic functions are constructed according to the relationship between a nature number and binary representation of divided regions,and the multiphase image segmentation problem is transformed into two-phase image segmentation problem.The label functions are vectorized,and the dual variables are introduced to transform the model into the continuous max-flow problem,which has the efficiency of graph cut(GC)algorithm,and overcomes the problem of accuracy degradation caused by discretization.2.The multiphase image segmentation Vese-Chan mode without redundant parameter estimation is proposed.When images exit redundant phases,to solve the problem of redundant parameter estimation,the area constraint for the redundant region is introduced in the paper.An automatic construction method for characteristic functions is used,which can keep the systematic expression of the characteristic functions,and represent and partition all regions including redundant regions.3.The Alternating Direction Method of Multipliers(ADMM)is designed for the proposed models to decompose the original energy functional problem into some simple optimization sub-problems,which can be solved by using Gauss-Seidel iterative method or generalized soft thresholding formulas.Finally,some numerical examples for gray images and color images are presented to demonstrate that the proposed models reduce the computational iterations and improve the computational efficiency based on maintaining the computational accuracy.