A Fractional Active Contour Model for Medical Image Segmentation

Consideration of both traditional local and global information for medical image segmentation remains a challenging task. Some hybrid methods have shown promise in handling this challenge. In this paper, a new hybrid method is presented, which incorporates image gradient, local information and global information into a framework. The energy or level-set function in the framework integrates fractional order differentiation, fractional order gradient magnitude, and difference image information into the well-known local Chan-Vese model, which has been shown to be effective and efficient in modeling the local information. The presented new model can also enhance low frequency information, which is clinically desired. Experiments on synthetic images as well as real images were performed to demonstrate the segmentation accuracy and computational efficiency of the presented hybrid method. The dice similarity coefficient merit was employed as the comparative quantitative measures and showed a noticeable gain over a current hybrid method.