Rueckert Daniel, Aljabar Paul, Heckemann Rolf A, Hajnal Joseph V, Hammers Alexander
Department of Computing, Imperial College London, UK.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):702-9. doi: 10.1007/11866763_86.
In this paper we propose a diffeomorphic non-rigid registration algorithm based on free-form deformations (FFDs) which are modelled by B-splines. In contrast to existing non-rigid registration methods based on FFDs the proposed diffeomorphic non-rigid registration algorithm based on free-form deformations (FFDs) which are modelled by B-splines. To construct a diffeomorphic transformation we compose a sequence of free-form deformations while ensuring that individual FFDs are one-to-one transformations. We have evaluated the algorithm on 20 normal brain MR images which have been manually segmented into 67 anatomical structures. Using the agreement between manual segmentation and segmentation propagation as a measure of registration quality we have compared the algorithm to an existing FFD registration algorithm and a modified FFD registration algorithm which penalises non-diffeomorphic transformations. The results show that the proposed algorithm generates diffeomorphic transformations while providing similar levels of performance as the existing FFD registration algorithm in terms of registration accuracy.
在本文中,我们提出了一种基于自由形式变形(FFD)的微分同胚非刚性配准算法,该自由形式变形由B样条曲线建模。与现有的基于FFD的非刚性配准方法不同,所提出的基于自由形式变形(FFD)的微分同胚非刚性配准算法由B样条曲线建模。为了构建微分同胚变换,我们组合了一系列自由形式变形,同时确保各个FFD是一一变换。我们在20幅正常脑磁共振图像上评估了该算法,这些图像已被手动分割为67个解剖结构。使用手动分割和分割传播之间的一致性作为配准质量的度量,我们将该算法与现有的FFD配准算法以及惩罚非微分同胚变换的改进FFD配准算法进行了比较。结果表明,所提出的算法生成微分同胚变换,同时在配准精度方面提供与现有FFD配准算法相似的性能水平。