Active contour model for simultaneous MR image segmentation and denoising

In this paper, a new region-based active contour model is proposed for magnetic resonance image segmentation and denoising based on the global minimization framework and level set evolution. A new region fitting energy based on Nadaraya–Watson estimator and local image information is defined to enforce the curve evolution. By this improved region fitting term, the images with noise and intensity un-uniformity can be segmented and denoised. Inspired by the Perona–Malik diffusion equation, an edge-preserving regularization term is defined through the duality formulation to penalize the length of region boundaries. By this new regularization term, the edge information is utilized to improve the contourʼs ability of capturing the edge and remaining smooth during the evolution. The energy functional of the proposed model is minimized by an efficient dual algorithm avoiding the inefficiency of the gradient descent method. Experiments on medical images demonstrate the proposed model provides a hybrid way to perform image segmentation and image denoising simultaneously.