Noisy image segmentation based on wavelet transform and active contour model

Abstract

This article addresses the noisy image segmentation problems based on wavelet transform and active contour model. In order to get better results, this article proposes a new segmentation and selective smoothing algorithm. First, a new adaptive segmentation model based on grey-level image segmentation model is proposed, and this model can also be extended to the vector value image segmentation. By virtue of the prior information of regions and boundary of image, a framework is established to construct different segmentation models using different probability density functions. A segmentation model exploiting Gaussian probability density function is given in this article. A penalizing term is employed to replace the time-consuming re-initialization process. An efficient and unconditional stable algorithm based on locally one-dimensional scheme is developed and it is used to segment the grey image and the vector value image. Second, in each stage of segmentation process, wavelet denoising algorithms are employed for different sub-regions independently, so that better segmentation and smoothing results can be obtained. Comparing with existing classical model, the proposed approach gives the best performance.