Consistent landmark and intensity-based image registration
Consistent landmark and intensity-based image registration
复制标题
DOI：
10.1109/tmi.2002.1009381
复制
发表时间：
2002-05-01
影响因子：
10.6
通讯作者：
Christensen, GE
中科院分区：
文献类型：
--
作者：
Johnson, HJ;Christensen, GE
提出了两种新的一致性图像配准算法：一种是基于特征点匹配的算法，另一种是基于特征点和灰度信息的匹配算法。一致的界标和强度配准算法通过匹配对应的界标在界标位置附近的图像之间产生良好的对应性，并且通过匹配图像强度在远离界标位置的图像之间产生良好的对应性。与类似的单向算法相比，这些新的一致性算法联合估计两幅图像之间的正向和反向变换，同时最小化反向一致性误差-正（反向）变换和反向（正）变换的逆之间的误差。这减少了与大的逆一致性误差相关联的正向变换和反向变换之间的模糊对应。在这两种算法中，薄板样条（TPS）模型用于正则化估计的转换。二维（2-D）的例子表明，传统的单向地标TPS算法产生的逆一致性误差可以是相对较大的，这种错误是最小化使用一致的地标算法。使用2-D磁共振成像数据的结果表明，使用地标和强度信息一起产生更好的医学图像之间的对应关系比单独使用地标或强度信息。
Two new consistent image registration algorithms are presented: one is based on matching corresponding landmarks and the other is based on matching both landmark and intensity information. The consistent landmark and intensity registration algorithm produces good correspondences between images near landmark locations by matching corresponding landmarks and away from landmark locations by matching the image intensities. In contrast to similar unidirectional algorithms, these new consistent algorithms jointly estimate the forward and reverse transformation between two images while minimizing the inverse consistency error-the error between the forward (reverse) transformation and the inverse of the the reverse (forward) transformation. This reduces the ambiguous correspondence between the forward and reverse transformations associated with large inverse consistency errors. In both algorithms a thin-plate spline (TPS) model is used to regularize the estimated transformations. Two-dimensional (2-D) examples are presented that show the inverse consistency error produced by the traditional unidirectional landmark TPS algorithm can be relatively large and that this error is minimized using the consistent landmark algorithm. Results using 2-D magnetic resonance imaging data are presented that demonstrate that using landmark and intensity information together produce better correspondence between medical images than using either landmarks or intensity information alone.