NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Magn Reson Imaging. 2014 Jan;32(1):71-83. doi: 10.1016/j.mri.2013.01.010. Epub 2013 Nov 13.
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.
磁共振（MR）图像分割是手术和治疗计划的关键步骤。在本文中，我们提出了一种基于水平集的分割方法，用于解决强度不均匀问题的 MR 图像。为了解决初始化敏感性问题，我们提出了一种新的图像引导正则化方法来限制水平集函数。最大后验推断被采用，将分割和偏置场校正统一在一个单一的框架内。在这个框架下，充分利用了轮廓先验和偏置场先验。结果，图像强度的不均匀性得到了很好的解决。提供了广泛的实验来评估所提出的方法，与其他最先进的方法相比，在分割和偏置场校正精度方面都有显著的提高。
