A robust level set method with Markov random fields term and fractional-order regularization term
Hao Sun, Weihua Liu, Guanghui Sun, Xianqiang Yang, Huiyan Zhang · IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society · 2017
In this paper, a robust level set method is proposed for image segmentation. Traditional level set methods are sensitive to noise in images which greatly limits its application in real project. To overcome this shortcoming, the fractional order regularization and Markov random fields term are incorporated into the traditional level methods in this paper. The fractional order regularization can reveal more details of the image and the Markov random field (MRF) term takes the hole image into account. In additional to these two terms, a region term and a penalty term are added into the energy function. The comparison of the proposed method with the classical level set method is made and the results show that the proposed method is robust to noise in images in image segmentation application.