An Adaptive Weighted Active Contour Segmentation Model for 3T/5T MRI from the Same Person

Image segmentation is a complex and core technique in the medical image domain. However, low-quality images, such as images with weak edges, may bring considerable challenges for radiologists. In this paper, we propose an adaptive weighted curvature-based active contour model by coupling heat kernel convolution and adaptively weighted high-order total variation to improve diagnosis effectiveness. The numerical experimental results on 3T/5T MRI datasets demonstrate that the proposed model is quite efficient and robust compared with several traditional segmentation methods, which would exert great value in quantitative image evaluation of MRI diagnosis for the same person.