Yang Yunyun, Tian Dongcai, Jia Wenjing, Shu Xiu, Wu Boying
School of Science, Harbin Institute of Technology, Shenzhen, China.
School of Science, Harbin Institute of Technology, Shenzhen, China.
Magn Reson Imaging. 2019 Apr;57:50-67. doi: 10.1016/j.mri.2018.10.005. Epub 2018 Oct 13.
At present, magnetic resonance (MR) images have gradually become a major aid for clinical medicine, which has greatly improved the doctor's diagnosis rate. Accurate and fast segmentation of MR images plays an extremely important role in medical research. However, due to the influence of external factors and the defects of imaging devices, the MR images have severe intensity inhomogeneity, which poses a great challenge to accurately segment MR images. To deal with this problem, this paper presents an improved active contour model by combining the level set evolution model (LSE) and the split Bregman method, and gives the two-phase, the multi-phase and the vector-valued formulations of our model, respectively. The use of the split Bregman method accelerates the minimization process of our model by reducing the computation time and iterative times. A slowly varying bias field is added into the energy functional, which is the key to correct inhomogeneous images. By estimating the bias fields, not only can we get accurate image segmentation results, but also a homogeneous image after correction is provided. Then we apply our model to segment a large amount of synthetic and real MR images, including gray and color images. Experimental results show that our model can provide satisfactory segmentation and correction results for both gray and color images. Besides, compared with the LSE model, our model has higher accuracy and is superior to the LSE model. In addition, experimental results also demonstrate that our model has the advantages of being insensitive to initial contours and robust to noises.
目前，磁共振（MR）图像已逐渐成为临床医学的主要辅助手段，极大地提高了医生的诊断率。磁共振图像的准确快速分割在医学研究中起着极其重要的作用。然而，由于外部因素的影响和成像设备的缺陷，磁共振图像存在严重的强度不均匀性，这对准确分割磁共振图像构成了巨大挑战。为解决这一问题，本文提出了一种将水平集演化模型（LSE）与分裂Bregman方法相结合的改进主动轮廓模型，并分别给出了该模型的两相、多相和向量值公式。分裂Bregman方法的使用通过减少计算时间和迭代次数加速了模型的最小化过程。在能量泛函中加入一个缓慢变化的偏置场，这是校正不均匀图像的关键。通过估计偏置场，我们不仅可以得到准确的图像分割结果，还能得到校正后的均匀图像。然后我们将模型应用于分割大量的合成和真实磁共振图像，包括灰度图像和彩色图像。实验结果表明，我们的模型对灰度图像和彩色图像都能提供令人满意的分割和校正结果。此外，与LSE模型相比，我们的模型具有更高的精度，优于LSE模型。此外，实验结果还表明，我们的模型具有对初始轮廓不敏感和对噪声鲁棒的优点。
