Image segmentation by phase-field models with local information

We proposed an improved phase-field model with local information for image segmentation. In our approach, a new double well potential function containing local contributions is taken into account. The advantages of the new scheme are in two folders. Firstly, the affection of noise could be reduced in image segmentation with intensity inhomogeneity. Secondly, a more stable boundary evolution could be achieved compared with the traditional way. Segmentation results for several different types of images are reported. We compare the segmentation results with the classical model for two-dimensional images. In the segmentation of three-dimensional magnetic resonance brain images, the accuracy comparisons are presented by the Dice and Jaccard similarity coefficients. Compared with the classical model, our model has higher segmentation accuracy and better stability of boundary evolution.