Wang Anna, Lu Dan, Wang Zhe, Fang Zhizhen
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2010 Aug;27(4):763-8, 784.
In allusion to non-rigid registration of medical images, the paper gives a practical feature points matching algorithm--the image registration algorithm based on the scale-invariant features transform (Scale Invariant Feature Transform, SIFT). The algorithm makes use of the image features of translation, rotation and affine transformation invariance in scale space to extract the image feature points. Bidirectional matching algorithm is chosen to establish the matching relations between the images, so the accuracy of image registrations is improved. On this basis, affine transform is chosen to complement the non-rigid registration, and normalized mutual information measure and PSO optimization algorithm are also chosen to optimize the registration process. The experimental results show that the method can achieve better registration results than the method based on mutual information.
针对医学图像的非刚性配准问题，本文给出了一种实用的特征点匹配算法——基于尺度不变特征变换（Scale Invariant Feature Transform，SIFT）的图像配准算法。该算法利用图像在尺度空间中具有平移、旋转和仿射变换不变性的特征来提取图像特征点。采用双向匹配算法建立图像间的匹配关系，从而提高了图像配准的准确性。在此基础上，选择仿射变换来补充非刚性配准，还选择归一化互信息测度和粒子群优化算法来优化配准过程。实验结果表明，该方法比基于互信息的方法能取得更好的配准效果。
