Department of Math, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Magn Reson Imaging. 2012 Jan;30(1):85-95. doi: 10.1016/j.mri.2011.09.003. Epub 2011 Nov 4.
Intensity inhomogeneities cause considerable difficulty in the quantitative analysis of magnetic resonance (MR) images. Thus, bias field correction is a necessary step before quantitative analysis of MR data can be undertaken. This paper presents an anisotropic approach to bias correction and segmentation for images with intensity inhomogeneities and noise. Intensity-based methods are usually applied to estimate the bias field; however, most of them only concern the intensity information. When the images have noise or slender topological objects, these methods cannot obtain accurate results or bias fields. We use structure information to construct an anisotropic Gibbs field and combine the anisotropic Gibbs field with the Bayesian framework to segment images while estimating the bias fields. Our method is able to capture bias of quite general profiles. Moreover, it is robust to noise and slender topological objects. The proposed method has been used for images of various modalities with promising results.
不均匀性会给磁共振（MR）图像的定量分析带来很大的困难。因此，在进行 MR 数据的定量分析之前，偏置场校正都是必要的步骤。本文提出了一种针对存在强度不均匀性和噪声的图像的各向异性偏置校正和分割方法。基于强度的方法通常用于估计偏置场，但它们中的大多数只考虑强度信息。当图像存在噪声或细长拓扑对象时，这些方法无法获得准确的结果或偏置场。我们使用结构信息来构建各向异性吉布斯场，并将各向异性吉布斯场与贝叶斯框架相结合，在估计偏置场的同时对图像进行分割。我们的方法能够捕捉到相当一般的轮廓的偏置。此外，它对噪声和细长拓扑对象具有鲁棒性。所提出的方法已用于各种模态的图像，取得了良好的效果。
