Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, ClearWater Bay, Hong Kong.
IEEE Trans Med Imaging. 2010 Jan;29(1):106-19. doi: 10.1109/TMI.2009.2028078. Epub 2009 Aug 7.
A new feature based nonrigid image registration method for magnetic resonance (MR) brain images is presented in this paper. Each image voxel is represented by a rotation invariant feature vector, which is computed by passing the input image volumes through a new bank of symmetric alpha stable (SalphaS) filters. There are three main contributions presented in this paper. First, this work is motivated by the fact that the frequency spectrums of the brain MR images often exhibit non-Gaussian heavy-tail behavior which cannot be satisfactorily modeled by the conventional Gabor filters. To this end, we propose the use of SalphaS filters to model such behavior and show that the Gabor filter is a special case of the SalphaS filter. Second, the maximum response orientation (MRO) selection criterion is designed to extract rotation invariant features for registration tasks. The MRO selection criterion also significantly reduces the number of dimensions of feature vectors and therefore lowers the computation time. Third, in case the segmentations of the input image volumes are available, the Fisher's separation criterion (FSC) is introduced such that the discriminating power of different feature types can be directly compared with each other before performing the registration process. Using FSC, weights can also be assigned automatically to different voxels in the brain MR images. The weight of each voxel determined by FSC reflects how distinctive and salient the voxel is. Using the most distinctive and salient voxels at the initial stage to drive the registration can reduce the risk of being trapped in the local optimum during image registration process. The larger the weight, the more important the voxel. With the extracted feature vectors and the associated weights, the proposed method registers the source and the target images in a hierarchical multiresolution manner. The proposed method has been intensively evaluated on both simulated and real 3-D datasets obtained from BrainWeb and Internet Brain Segmentation Repository (IBSR), respectively, and compared with HAMMER, an extended version of HAMMER based on local histograms (LHF), FFD, Demons, and the Gabor filter based registration method. It is shown that the proposed method achieves the highest registration accuracy among the five widely used image registration methods.
本文提出了一种基于新特征的磁共振（MR）脑图像非刚性配准方法。每个图像体素都由一个旋转不变特征向量表示，该向量通过输入图像体积通过新的对称 alpha 稳定（SalphaS）滤波器组计算得到。本文主要有三个贡献。首先，这项工作的动机是，大脑 MR 图像的频率谱通常表现出非高斯重尾行为，而传统的 Gabor 滤波器无法令人满意地对其进行建模。为此，我们提出使用 SalphaS 滤波器来对这种行为进行建模，并表明 Gabor 滤波器是 SalphaS 滤波器的特例。其次，设计了最大响应方向（MRO）选择准则，以提取旋转不变特征用于配准任务。MRO 选择准则还显著减少了特征向量的维数，从而降低了计算时间。第三，如果输入图像体积的分割可用，则引入 Fisher 分离准则（FSC），以便在执行配准过程之前，可以直接比较不同特征类型的辨别能力。使用 FSC，还可以自动为大脑 MR 图像中的不同体素分配权重。FSC 确定的每个体素的权重反映了该体素的独特性和显著性。在初始阶段使用最独特和最显著的体素来驱动配准，可以降低图像配准过程中陷入局部最优的风险。权重越大，体素越重要。利用提取的特征向量和相关权重，该方法以分层多分辨率的方式对源图像和目标图像进行配准。该方法已在分别来自 BrainWeb 和 Internet Brain Segmentation Repository（IBSR）的模拟和真实 3D 数据集上进行了深入评估，并与 HAMMER、基于局部直方图（LHF）的 HAMMER 扩展版本、FFD、Demons 和基于 Gabor 滤波器的配准方法进行了比较。结果表明，该方法在五种广泛使用的图像配准方法中实现了最高的配准精度。
