Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, Room Q0S.459, 3584 CX Utrecht, The Netherlands.
Med Image Anal. 2012 Dec;16(8):1521-31. doi: 10.1016/j.media.2012.06.010. Epub 2012 Jul 24.
A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal multi-feature model, classifies local alignments into three quality categories: correct, poor or wrong alignment. We establish a reference registration error set as basis for training and testing of the method. It consists of image registrations obtained from different non-rigid registration algorithms and manually established point correspondences of automatically determined landmarks. We employ a set of different classifiers and evaluate the performance of the proposed image features based on the classification performance of corresponding single-feature classifiers. Feature selection is conducted to find an optimal subset of image features and the resulting multi-feature model is validated against the set of single-feature classifiers. We consider the setup generic, however, its application is demonstrated on 51 CT follow-up scan pairs of the lung. On this data, the proposed method performs with an overall classification accuracy of 90%.
提出了一种用于医学图像配准自动质量评估的新方法。该方法基于对局部对齐模式的监督学习，这些模式由在独特的地标点处捕获的统计图像特征捕获。采用最优多特征模型的两级分类器级联将局部对齐分为三个质量类别：正确、较差或错误对齐。我们建立了一个参考配准误差集作为该方法的训练和测试基础。它由从不同的非刚性配准算法获得的图像配准和通过自动确定的地标手动建立的点对应组成。我们使用了一组不同的分类器，并根据相应的单特征分类器的分类性能评估所提出的图像特征的性能。进行特征选择以找到图像特征的最优子集，并且针对单特征分类器集验证所得到的多特征模型。然而，我们认为该设置是通用的，但其应用在 51 对肺部 CT 随访扫描对中进行了演示。在该数据上，所提出的方法的整体分类准确性为 90%。
