A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation

In medical field, it remains challenging to accurately segment medical images due to low contrast, complex noises and intensity inhomogeneity. To overcome these obstacles, this paper provides a novel edge-based active contour model (ACM) for medical image segmentation. Specifically, an accurate regularization approach is presented to maintain the level set function with a signed distance property, which guarantees the stability of the evolution curve and the accuracy of the numerical computation. More significantly, an adaptive perturbation is integrated into the framework of the edge-based ACM. The perturbation technique can balance the stability of curve evolution and the accuracy of segmentation, which is key for segmenting medical images with intensity inhomogeneity. A number of experiments on both artificial and real medical images demonstrate that the proposed segmentation model outperforms state-of-the-art methods in terms of robustness to noise and segmentation accuracy.