Styner M, Brechbühler C, Székely G, Gerig G
Department of Computer Science, University of North Carolina at Chapel Hill, 27514, USA.
IEEE Trans Med Imaging. 2000 Mar;19(3):153-65. doi: 10.1109/42.845174.
This paper presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. We present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.
本文提出了一种校正磁共振成像（MRI）中强度不均匀性的新方法，该方法显著改善了基于强度的组织分割。低频偏置场导致的图像亮度值失真妨碍了视觉检查和分割。这种名为参数偏置场校正（PABIC）的新校正方法基于成像过程的简化模型、组织类别统计的参数模型以及不均匀性场的多项式模型。我们假设图像由分配给少量具有先验已知统计信息类别的像素组成。此外，我们假设图像受到噪声和低频不均匀性场的影响。使用进化策略（ES）将参数偏置场的估计公式化为一个非线性能量最小化问题。所得的偏置场与图像区域配置无关，因此克服了基于同态滤波方法的局限性。此外，PABIC可以校正比图像对比度大得多的偏置失真。输入参数是类别的强度统计信息和多项式函数的次数。多项式方法将偏置校正与直方图调整相结合，使其非常适合对来自系列研究的数据集的强度直方图进行归一化。我们展示了模拟结果以及使用体模和测试图像进行的定量验证。大量使用乳腺、表面和头部线圈采集的二维和三维MR图像数据已得到处理，证明了这种新偏置校正方案的通用性和鲁棒性。
