IEEE Trans Image Process. 2017 Oct;26(10):4900-4910. doi: 10.1109/TIP.2017.2722689. Epub 2017 Jul 3.
We present a method for image registration based on 3D scale- and rotation-invariant keypoints. The method extends the scale invariant feature transform (SIFT) to arbitrary dimensions by making key modifications to orientation assignment and gradient histograms. Rotation invariance is proven mathematically. Additional modifications are made to extrema detection and keypoint matching based on the demands of image registration. Our experiments suggest that the choice of neighborhood in discrete extrema detection has a strong impact on image registration accuracy. In head MR images, the brain is registered to a labeled atlas with an average Dice coefficient of 92%, outperforming registration from mutual information as well as an existing 3D SIFT implementation. In abdominal CT images, the spine is registered with an average error of 4.82 mm. Furthermore, keypoints are matched with high precision in simulated head MR images exhibiting lesions from multiple sclerosis. These results were achieved using only affine transforms, and with no change in parameters across a wide variety of medical images. This paper is freely available as a cross-platform software library.
我们提出了一种基于 3D 尺度和旋转不变关键点的图像配准方法。该方法通过对方向分配和梯度直方图进行关键修改，将尺度不变特征变换（SIFT）扩展到任意维度。旋转不变性通过数学证明。根据图像配准的要求，对极值检测和关键点匹配进行了额外的修改。我们的实验表明，离散极值检测中邻域的选择对图像配准精度有很大的影响。在头部磁共振图像中，大脑以平均骰子系数 92%的精度注册到一个带标签的图谱，优于互信息注册以及现有的 3D SIFT 实现。在腹部 CT 图像中，脊柱的平均误差为 4.82 毫米。此外，在模拟的头部磁共振图像中，即使存在多发性硬化症引起的病变，关键点也能高精度匹配。这些结果仅使用仿射变换获得，并且在各种医学图像中无需更改参数。本文作为一个跨平台的软件库免费提供。
