Vovk Uros, Pernus Franjo, Likar Bostjan
Faculty of Electrical Engineering, University of Ljubljana, Trzaska 25, 1000 Ljubljana, Slovenia.
Phys Med Biol. 2004 Sep 7;49(17):4119-33. doi: 10.1088/0031-9155/49/17/020.
We propose a novel fully automated method for retrospective correction of intensity inhomogeneity, which is an undesired phenomenon in many automatic image analysis tasks, especially if quantitative analysis is the final goal. Besides most commonly used intensity features, additional spatial image features are incorporated to improve inhomogeneity correction and to make it more dynamic, so that local intensity variations can be corrected more efficiently. The proposed method is a four-step iterative procedure in which a non-parametric inhomogeneity correction is conducted. First, the probability distribution of image intensities and corresponding second derivatives is obtained. Second, intensity correction forces, condensing the probability distribution along the intensity feature, are computed for each voxel. Third, the inhomogeneity correction field is estimated by regularization of all voxel forces, and fourth, the corresponding partial inhomogeneity correction is performed. The degree of inhomogeneity correction dynamics is determined by the size of regularization kernel. The method was qualitatively and quantitatively evaluated on simulated and real MR brain images. The obtained results show that the proposed method does not corrupt inhomogeneity-free images and successfully corrects intensity inhomogeneity artefacts even if these are more dynamic.
我们提出了一种全新的全自动方法，用于对强度不均匀性进行回顾性校正，强度不均匀性在许多自动图像分析任务中是一种不良现象，特别是当定量分析是最终目标时。除了最常用的强度特征外，还纳入了额外的空间图像特征，以改善不均匀性校正并使其更具动态性，从而能够更有效地校正局部强度变化。所提出的方法是一个四步迭代过程，其中进行了非参数不均匀性校正。首先，获取图像强度的概率分布及其相应的二阶导数。其次，为每个体素计算沿强度特征压缩概率分布的强度校正力。第三，通过对所有体素力进行正则化来估计不均匀性校正场，第四，进行相应的局部不均匀性校正。不均匀性校正动态程度由正则化核的大小决定。该方法在模拟和真实的脑部磁共振图像上进行了定性和定量评估。所得结果表明，所提出的方法不会破坏无不均匀性的图像，即使强度不均匀性伪影更具动态性，也能成功校正。
