A New Segmentation Model of Gray Non- uniform Images
Y Wang · Jisuanji gongcheng · 2015
Local region-based Active Contour Model(ACM) is easily influenced by the location of the initial curves w hen it segments images with intensity inhomogeneity and its numerical implementation based on Level Set( LS) method is low er. For this,a new segmentation model is proposed in this paper. The model includes the Local Signed Difference(LSD) energy as data driven term for curve evolution. In order to reduce the dependence on the location of the initial curve,a Globally Convex Segmentation( GCS) scheme is used to derive a discrete convex segmentation model. The new model includes a second order smooth term from Mumford-Shah segmentation model to make the segmented regions smoother. It uses split Bregman iterations to get a fast numerical implementation. Compared with the Local Binary Fitting(LBF) model,LSD model,experimental results show that the model can segment images with intensity inhomogeneity correctly,and is more efficient and more robust.