关键词: 水平集, 谱聚类, nystrom方法, 余弦相似度, [k]-means

Abstract: The existing Region Scalable Fitting（RSF） energy model is based on the approximation of the gray value inside and outside the initial contour, which better deals with the problem of image gray unevenness existing in image segmentation. However, when choosing an inappropriate initial contour, it is easy to fall into the local minimum due to the non-convexity of the energy functional of the RSF model. In order to ensure the robustness of initialization, a RSF model with fitting function optimization is proposed. In the process of curve evolution, a function is added in the opposite direction of the evolution direction to exchange the internal and external fitting values of the curve, so that the whole curve evolves along the ipsilateral boundary of the object. Spectral theory is introduced into the model to enable it to cluster large data samples and quickly converge to global optimal solutions. The improved model is applied to medical image segmentation. The experimental results show that the model obtains more robust segmentation results and higher segmentation efficiency than RSF model.

Key words: level set method, spectral clustering, nystrom method, cosine similarity, [k]-means