In order to solve the problem of deformable objects segmentation with a fixed shape prior

a shape prior constrained and parameter adaption level set segmentation method based on kernel principal component analysis(KPCA)is proposed. First

the KPCA method is used to get the base vectors in the shape prior feature space. Then

the Parzen window method is used to estimate the results of the original image for image data term and an affine transformation is performed to align the image region of interest and prior shape training set to add shape priors to the segmentation model. At last

a parameter adaptive method is introduced when solving the evolution equation based on level set method. Experimental results show that our method can effectively segment objects with different attitudes in comparison with the Chan-Vese (CV) model and single prior shape constrained level set methods.