Variational image segmentation methods

which are based on energy minimization process

have received significant attentions for years and gained fruitful achievements. However

the use of image information alone often leads to poor segmentation and results in presence of noise

clutter

or occlusion. Introducing shape prior to contour evolution process has been shown as an effective way to address these problems. However

problems associated with this method is nontrivial. The traditional solution is to estimate several pose parameters within each step of level set iteration. This solution is complicated and time consuming. Based on the kernel principal component analysis (KPCA) shape model

we propose a novel KPCA-based shape prior model with intrinsic pose invariance

and we then combine it with C-V image segmentation model. The complete segmentation model explicitly eliminates pose parameter estimation during level set iteration. Furthermore

segmenting correct ratio is increased by 7.47% compared with C-V model. We present an adaptive method to calculate parameter for the Gaussian kernel in KPCA shape model. Experimental results show the robustness of the combined model against noise

clutter

or occlusion and the ability to deal with the affine pose variance between prior shapes and object to be detected.