A new correntropy-based level set algorithm using local robust statistics information
Sheng Wang, Xiaoliang Jiang · International Journal of High Performance Systems Architecture · 2020
Intensity inhomogeneity or noise usually appear in various kinds of images, which cause a challenging task in image segmentation. To solve these issues, a novel correntropy-based level set algorithm utilising local robust statistics information is introduced. In the proposed method, the modified local image fitting (MLIF) equation is built by describing the difference between the images of fitted and local robust statistics. Then, by using the correntropy criterion, the MLIF model can automatically emphasise the weight coefficient of the samples that are approximately to the grey means. In this case, the new guided energy term can accurately process images with weak edge and more adaptive to noise. Finally, we introduce a level set regularisation terms to remove re-initialisation process. Experiments on a lot of images demonstrate our method has good segmentation ability on the part of visual perception and robustness, as compared with traditional algorithms.