A spline-based non-linear diffeomorphism for multimodal prostate registration
A spline-based non-linear diffeomorphism for multimodal prostate registration
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DOI：
10.1016/j.media.2012.04.006
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发表时间：
2012-08-01
影响因子：
10.9
通讯作者：
Meriaudeau, Fabrice
中科院分区：
文献类型：
--
作者：
Mitra, Jhimli;Kato, Zoltan;Meriaudeau, Fabrice
提出了一种基于非线性正则化框架的经直肠超声和磁共振前列腺图像的非刚性配准方法，该框架由形状背景的统计度量获得。分割的前列腺形状由形状上下文表示，形状表示之间的Bhattacharyya距离被用来寻找2D固定图像和运动图像之间的点对应。该配准方法涉及多峰图像之间非线性微分同胚的参数估计，其基础是求解薄板样条的一组非线性方程。该解是通过对固定和运动图像上的一组非线性函数进行积分而构造的超定非线性方程组的最小二乘解。然而，这可能不会导致临床上可接受的解剖靶点的改变。因此，薄板花键的正则化弯曲能量以及建立的对应关系的局部化误差应包含在方程组中。在20对前列腺中段超声和磁共振图像上对该方法的配准精度进行了评估。结果表明，Dice相似性系数平均为0.980+/-0.004，平均95%Hausdorff距离为1.63+/-0.48 mm，目标配准和定位误差分别为1.60+/-1.17 mm和0.15+/-0.12 mm。(C)2012爱思唯尔B.V.保留所有权利。
This paper presents a novel method for non-rigid registration of transrectal ultrasound and magnetic resonance prostate images based on a non-linear regularized framework of point correspondences obtained from a statistical measure of shape-contexts. The segmented prostate shapes are represented by shape-contexts and the Bhattacharyya distance between the shape representations is used to find the point correspondences between the 2D fixed and moving images. The registration method involves parametric estimation of the non-linear diffeomorphism between the multimodal images and has its basis in solving a set of non-linear equations of thin-plate splines. The solution is obtained as the least-squares solution of an over-determined system of non-linear equations constructed by integrating a set of non-linear functions over the fixed and moving images. However, this may not result in clinically acceptable transformations of the anatomical targets. Therefore, the regularized bending energy of the thin-plate splines along with the localization error of established correspondences should be included in the system of equations. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate mid-gland ultrasound and magnetic resonance images. The results obtained in terms of Dice similarity coefficient show an average of 0.980 +/- 0.004, average 95% Hausdorff distance of 1.63 +/- 0.48 mm and mean target registration and target localization errors of 1.60 +/- 1.17 mm and 0.15 +/- 0.12 mm respectively. (c) 2012 Elsevier B.V. All rights reserved.