Image-guided regularization level set evolution for MR image segmentation and bias field correction.
Magnetic resonance (MR) image segmentation is a crucial step in surgical and treatment planning. In this paper, we propose a level-set-based segmentation method for MR images with intensity inhomogeneous problem. To tackle the initialization sensitivity problem, we propose a new image-guided regularization to restrict the level set function. The maximum a posteriori inference is adopted to unify segmentation and bias field correction within a single framework. Under this framework, both the contour prior and the bias field prior are fully used. As a result, the image intensity inhomogeneity can be well solved. Extensive experiments are provided to evaluate the proposed method, showing significant improvements in both segmentation and bias field correction accuracies as compared with other state-of-the-art approaches.
NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. Electronic address: lfwang@nlpr.ia.ac.cn.
Lingfeng
L
Wang
Chunhong
C
Pan
D000465:Algorithms; D016880:Anisotropy; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D012984:Software
Bias field correction; Image-guided regularization; Level set; MR image segmentation