An efficient similarity-based level set model for medical image segmentation

It is usually difficult to correctly segment medical images with intensity inhomogeneity, which is of great significance in understanding of medical images. The local image intensity features play a vital role in accurately segmenting medical images with intensity inhomogeneity. Therefore, it is crucial to acquire the local intensity features for a deeper understanding of medical images. The main idea of this paper is to construct an efficient similarity-based level set model, which synthesizes the similarity theory, curve evolution and level set. Firstly, a local statistical function is modeled as different scales of Gaussian distributions to estimate bias fields, in which a real image can be approximately obtained for a more accurate medical image segmentation. Secondly, a new potential function is constructed to maintain the stability of the curve evolution, especially the signed distance profile in the neighborhood of the zero level set, which plays an important role in the correct segmentation. Thirdly, an adaptive condition criterion has been proposed to accelerate the convergence in the curve processing. Finally, the experiments on artificial and medical images and comparisons with the current well-known region-based models are discussed in details. Our extensive experimental results demonstrate that the proposed method can correctly segment medical images with intensity inhomogeneity in a few iterations and also is less sensitive to the initial contour.