Nowadays, typical active contour models are widely applied in image segmentation. However, they perform badly on real images with inhomogeneous subregions. In order to overcome the drawback, this paper proposes an edge-preserving smoothing image segmentation algorithm. At first, this paper analyzes the edge-preserving smoothing conditions for image segmentation and constructs an edge-preserving smoothing model inspired by total variation. The proposed model has the ability to smooth inhomogeneous subregions and preserve edges. Then, a kind of clustering algorithm, which reasonably trades off edge-preserving and subregion-smoothing according to the local information, is employed to learn the edge-preserving parameter adaptively. At last, according to the confidence level of segmentation subregions, this paper constructs a smoothing convergence condition to avoid oversmoothing. Experiments indicate that the proposed algorithm has superior performance in precision, recall, and F-measure compared with other segmentation algorithms, and it is insensitive to noise and inhomogeneous-regions.
The non-local means (NLM) filter has been proven to be an efficient feature-preserved denoising method and can be
applied to remove noise in the magnetic resonance (MR) images. To suppress noise more efficiently, we present a novel NLM
filter by using a low-pass filtered and low dimensional version of neighborhood for calculating the similarity weights. The
discrete cosine transform (DCT) is used as a smoothing kernel, allowing both improvements in similarity estimation and
computational speed-up. Experimental results show that the proposed filter achieves better denoising performance in MR
Images compared to others filters, such as recently proposed NLM filter and unbiased NLM (UNLM) filter.