Active contour model driven by local entropy energy
基于局部熵的主动轮廓模型

Using the C-V model to segment images with intensity inhomogeneity, the segmentation results are often not very good. Therefore, we propose an active contour model based on the local entropy energy. First, we introduce the concept of local entropy into the C-V model to get inhomogeneity information in local regions according to the kernel function and to model the local entropy energy function. Second, we use a variable level set to minimize the local entropy function and to get the gradient descent flow of the level set. Finally, simulation experiments are carried out on four severe intensity inhomogeneity images, and the results are compared to the proposed method with LBF and LGDF methods. It is shown that our method achieves more accurate segmentation results for intensity inhomogeneity images compared to the LBF and LGDF methods.