Robust image segmentation using local robust statistics and correntropy-based K-means clustering

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It is an important work to segment the real world images with intensity inhomogeneity such as magnetic resonance (MR) and computer tomography (CT) images. In practice, such images are often polluted by noise which make them difficult to be segmented by traditional level set based segmentation models. In this paper, we propose a robust level set image segmentation model combining local with global fitting energies to segment noised images. In the proposed model, the local fitting energy is based on the local robust statistics (LRS) information of an input image, which can efficiently reduce the effects of the noise, and the global fitting energy utilizes the correntropy-based K-means (CK) method, which can adaptively emphasize the samples that are close to their corresponding cluster centers. By integrating the advantages of global information and local robust statistics characteristics, the proposed model can efficiently segment images with intensity inhomogeneity and noise. Then, a level set regularization term is used to avoid re-initialization procedures in the process of curve evolution. In addition, the Gaussian filter is utilized to keep the level set smoothing in the curve evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. Experimental results show the advantages of our model in terms of accuracy and robustness to the noise. In particular, our method has been applied on some synthetic and real images with desirable results.