Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage
Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage
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DOI：
10.1109/4233.992165
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发表时间：
2002-03-01
影响因子：
--
通讯作者：
Toga, AW
中科院分区：
文献类型：
--
作者：
Dinov, ID;Mega, MS;Toga, AW
在基于模板的医学图像分析领域中，图像配准和归一化经常用于评估和解释标准模板或参考图谱空间中的数据。尽管大量的图像配准（翘曲）技术最近在文献中，只有少数研究已经进行了数值表征和比较各种对齐方法。在本文中，我们介绍了一种新的方法来分析图像配准的基础上选择性小波重建技术，使用频率自适应小波收缩。我们研究了四个多项式为基础的和两个更高的复杂性非仿射翘曲方法适用于组的立体定位人脑结构（磁共振成像）和功能（正电子发射断层扫描）的数据。根据图像配准的目的，我们提出了几种翘曲分类方案。我们的方法使用压缩小波空间中的本地和重新切片（前和后翘曲）数据的简洁表示来评估配准质量。这种技术是计算成本低，并利用图像压缩，图像增强，和基于小波的函数表示的去噪特性，以及频率相关的小波收缩的最优属性。
In the field of template-based medical image analysis, image registration and normalization are frequently used to evaluate and interpret data in a standard template or reference atlas space. Despite the large number of image-registration (warping) techniques developed recently in the literature, only a few studies have been undertaken to numerically characterize and compare various alignment methods. In this paper, we introduce a new approach for analyzing image registration based on a selective-wavelet reconstruction technique using a frequency-adaptive wavelet shrinkage. We study four polynomial-based and two higher complexity nonaffine warping methods applied to groups of stereotaxic human brain structural (magnetic resonance imaging) and functional (positron emission tomography) data. Depending upon the aim of the image registration, we present several warp classification schemes. Our method uses a concise representation of the native and resliced (pre- and post-warp) data in compressed wavelet space to assess quality of registration. This technique is computationally inexpensive and utilizes the image compression, image enhancement, and denoising characteristics of the wavelet-based function representation, as well as the optimality properties of frequency-dependent wavelet shrinkage.