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Non-rigid image registration based on Overlapped Block Check and Free-Form Deformation

机译:基于重叠块检查和自由格式变形的非刚性图像配准

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A new non-rigid registration approach is presented for the images which contain highly local deformation, using Free-Form Deformation (FFD) and Overlapped Block Check method. First of all, the hierarchical B-spline FFD with large grid is used for global registration. Then the rough registered image and fixed image are divided into a series of corresponding overlapped block pairs. We calculate the difference of each block pair and decide which ones need further refined registration, then a small grid FFD is adopted for them. In order to avoid the boundary effect, we propose a strategy to regulate the motion of the control points near the boundaries. Experimental results on Lena image and Brain image show that the proposed method can achieve high registration accuracy not only through difference images between registered images and fixed images, but also by similarity measures, such as Mutual Information (MI), Normalized Mutual Information (NMI) and Sum of Square Difference (SSD), etc. Furthermore, we confirm that the large and small spacing of control points in FFD will influence the accuracy of image registration in different ways.
机译:使用自由形式变形(FFD)和重叠块检查方法,针对包含高度局部变形的图像,提出了一种新的非刚性配准方法。首先,具有大网格的分层B样条FFD用于全局注册。然后,将粗糙的配准图像和固定图像划分为一系列相应的重叠块对。我们计算每个块对的差异,并确定哪些块需要进一步完善配准,然后为它们采用小网格FFD。为了避免边界效应,我们提出了一种策略来调节边界附近控制点的运动。在Lena图像和Brain图像上的实验结果表明,该方法不仅可以通过配准图像和固定图像之间的差异图像,而且可以通过互信息(MI),归一化互信息(NMI)等相似性度量来实现较高的配准精度。此外,我们确认,FFD中控制点的大小间距将以不同方式影响图像配准的准确性。

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