Noisy Image Segmentation by Modified Snake Model

A novel segmentation scheme for noisy image is proposed. According to the analysis of wavelet denoising method and multiscale geometric analysis techniques, an improved wavelet denoising algorithm combined with multiscale geometric analysis is presented in this paper first. Due to the isotropic nature of wavelet transform, 2D image details are not well represented in wavelet transform, which results in over smoothing. In this new denoising method, a noisy image is processed by the wavelet denoising method first, and then edges’ information which has been wrongly discarded, is picked up from the residue image by multiscale geometric analysis. The final denoising image is a combination of the wavelet denoising result and the edges’ information. Furthermore, incorporating prior knowledge on the contours’ shape and shape similarity metric based on Fourier descriptors of snakes, a parameter-varying snake model is introduced. It addresses the problem of varying parameters during snake method. Extensive experimental results illustrate the excellent performance.