School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China.
Magn Reson Imaging. 2013 Apr;31(3):439-47. doi: 10.1016/j.mri.2012.08.002. Epub 2012 Dec 7.
In this paper, we propose an improved variational level set approach to correct the bias and to segment the magnetic resonance (MR) images with inhomogeneous intensity. First, we use a Gaussian distribution with bias field as a local region descriptor in two-phase level set formulation for segmentation and bias field correction of the images with inhomogeneous intensities. By using the information of the local variance in this descriptor, our method is able to obtain accurate segmentation results. Furthermore, we extend this method to three-phase level set formulation for brain MR image segmentation and bias field correction. By using this three-phase level set function to replace the four-phase level set function, we can reduce the number of convolution operations in each iteration and improve the efficiency. Compared with other approaches, this algorithm demonstrates a superior performance.
在本文中，我们提出了一种改进的变分水平集方法来校正偏差，并分割具有不均匀强度的磁共振（MR）图像。首先，我们在两相水平集公式中使用具有偏差场的高斯分布作为局部区域描述符，用于分割和校正具有不均匀强度的图像的偏差场。通过使用该描述符中的局部方差信息，我们的方法能够获得准确的分割结果。此外，我们将该方法扩展到三相水平集公式，用于脑 MR 图像分割和偏差场校正。通过使用这个三相水平集函数来代替四相水平集函数，我们可以减少每个迭代中的卷积运算次数，提高效率。与其他方法相比，该算法表现出了更好的性能。
