Level set evolution with locally linear classification for image segmentation
Ying Wang, Lingfeng Wang, Shiming Xiang, Chunhong Pan · 2011
This paper presents a novel local region-based level set model for image segmentation. In each local region, we define a locally weighted least squares energy to fit a linear classification function. The local energy is then integrated over the entire image domain to form an energy functional in terms of level set function. The energy minimization is achieved by level set evolution and estimation of parameters of the locally linear function in an iterative process. By introducing the locally linear functions to separate background and foreground in local regions, our model not only ensures the accuracy of the segmentation results, but also be very robust to initialization. Experiments are reported to demonstrate the effectiveness and efficiency of our model.