Huang Chencheng, Zeng Li
Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing, 400044, China; Engineering Research Center of Industrial Computed Tomography Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing, 400044, China.
Key Laboratory of Optoelectronic Technology and System of the Education Ministry of China, Chongqing University, Chongqing, 400044, China; College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China.
PLoS One. 2015 Apr 2;10(3):e0120399. doi: 10.1371/journal.pone.0120399. eCollection 2015.
Intensity inhomogeneity causes many difficulties in image segmentation and the understanding of magnetic resonance (MR) images. Bias correction is an important method for addressing the intensity inhomogeneity of MR images before quantitative analysis. In this paper, a modified model is developed for segmenting images with intensity inhomogeneity and estimating the bias field simultaneously. In the modified model, a clustering criterion energy function is defined by considering the difference between the measured image and estimated image in local region. By using this difference in local region, the modified method can obtain accurate segmentation results and an accurate estimation of the bias field. The energy function is incorporated into a level set formulation with a level set regularization term, and the energy minimization is conducted by a level set evolution process. The proposed model first appeared as a two-phase model and then extended to a multi-phase one. The experimental results demonstrate the advantages of our model in terms of accuracy and insensitivity to the location of the initial contours. In particular, our method has been applied to various synthetic and real images with desirable results.
强度不均匀性给图像分割和磁共振（MR）图像理解带来了诸多困难。在进行定量分析之前，偏置校正 是解决MR图像强度不均匀性的一种重要方法。本文提出了一种改进模型，用于分割存在强度不均匀性的图像并同时估计偏置场。在改进模型中，通过考虑局部区域中测量图像与估计图像之间的差异来定义聚类准则能量函数。利用局部区域的这种差异，改进方法能够获得准确的分割结果和对偏置场的准确估计。该能量函数被纳入带有水平集正则项的水平集公式中，并且通过水平集演化过程进行能量最小化。所提出的模型最初是作为一个两相模型出现的，随后扩展为多相模型。实验结果证明了我们模型在准确性以及对初始轮廓位置不敏感方面的优势。特别是，我们的方法已应用于各种合成图像和真实图像，并取得了理想的结果。
