Learned-Miller Erik G, Jain Vidit
Department of Computer Science, University of Massachusetts, Amherst, MA 01003, USA.
Inf Process Med Imaging. 2005;19:615-26. doi: 10.1007/11505730_51.
The correction of multiplicative bias in magnetic resonance images is an important problem in medical image processing, especially as a preprocessing step for quantitative measurements and other numerical procedures. Most previous approaches have used a maximum likelihood method to increase the probability of the pixels in a single image by adaptively estimating a correction to the unknown image bias field. The pixel probabilities are defined either in terms of a pre-existing tissue model, or nonparametrically in terms of the image's own pixel values. In both cases, the specific location of a pixel in the image does not influence the probability calculation. Our approach, similar to methods of joint registration, simultaneously eliminates the bias from a set of images of the same anatomy, but from different patients. We use the statistics from the same location across different patients' images, rather than within an image, to eliminate bias fields from all of the images simultaneously. Evaluating the likelihood of a particular voxel in one patient's scan with respect to voxels in the same location in a set of other patients' scans disambiguates effects that might be due to either bias fields or anatomy. We present a variety of "two-dimensional" experimental results (working with one image from each patient) showing how our method overcomes serious problems experienced by other methods. We also present preliminary results on full three-dimensional volume correction across patients.
磁共振图像中乘法偏差的校正问题是医学图像处理中的一个重要问题，尤其是作为定量测量和其他数值程序的预处理步骤。大多数先前的方法都使用最大似然法，通过自适应估计对未知图像偏差场的校正来提高单幅图像中像素的概率。像素概率要么根据预先存在的组织模型来定义，要么根据图像自身的像素值以非参数方式来定义。在这两种情况下，图像中像素的具体位置都不会影响概率计算。我们的方法类似于联合配准方法，能同时消除来自同一解剖结构但不同患者的一组图像中的偏差。我们利用不同患者图像中相同位置的统计数据，而非单幅图像内的数据，来同时消除所有图像中的偏差场。通过评估某一患者扫描中特定体素相对于一组其他患者扫描中相同位置体素的似然性，可区分可能由偏差场或解剖结构导致的影响。我们展示了各种“二维”实验结果（使用每位患者的一幅图像），表明我们的方法如何克服其他方法所遇到的严重问题。我们还展示了跨患者进行全三维体积校正的初步结果。
