Multiphase Level Set-based Multi-objective Image Segmentation Cooperating with Spatial Fuzzy C-means

Abstract

Multi-objective image segmentation is an important problem in machine vision. Multiphase level set model is sensitive to the initial contours of zero level set functions and image noise. Inappropriate initial position of zero level set or large image noise will lead to wrong segmentation results. A multiphase level set model-based multi-objective image segmentation method cooperating with spatial fuzzy C-means clustering,i.e. SFCM-MLS,is proposed. Coarse segmentation results are acquired with spatial fuzzy C-means clustering. Initial level set functions of multiphase level set model are defined with these coarse results in order to finish the accurate segmentation. The proposed SFCM-MLS cooperating image segmentation algorithm is tested on magnetic resonance image of human brain and CT image of human liver with tumours. Experiment results indicate that compared with classical MLS,SFCM-MLS algorithm is insensitive to initial contours of zero level set and improves the accuracy of multi-objective image segmentation.