Markov random field based automatic image alignment for electron tomography
Markov random field based automatic image alignment for electron tomography
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DOI：
10.1016/j.jsb.2007.07.007
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发表时间：
2008-03-01
影响因子：
3
通讯作者：
Horowitz, Mark
中科院分区：
文献类型：
--
作者：
Amat, Fernando;Moussavi, Farshid;Horowitz, Mark
我们提出了一种断层扫描倾斜系列图像自动全精度对齐的方法。由于电子剂量有限和图像对比度低，迄今为止，冷冻电子显微镜图像的全精度自动对准仍然是一个艰巨的挑战。这些事实导致图像中的信噪比 (SNR) 较差，从而导致自动特征跟踪器产生错误，即使使用高对比度金颗粒作为基准特征也是如此。为了实现全精度重建的全自动对齐，我们在概率上将问题描述为在给定一组噪声图像的情况下找到最可能的粒子轨迹，使用上下文信息使解决方案对每个图像中的噪声更加鲁棒。为了解决这个最大似然问题，我们使用马尔可夫随机场（MRF）来建立对齐特征的对应关系和投影模型估计的鲁棒优化。由此产生的算法称为断层扫描重建的鲁棒对齐和投影估计（RAPTOR），不需要对我们尝试过的困难数据集进行任何手动干预，并且提供了与专家用户手动方法一样好的子像素对齐。我们能够自动绘制完整和部分标记轨迹，从而获得高度准确的图像对齐。我们的方法已应用于具有挑战性的冷冻电子断层扫描数据集，其信噪比较低，来自完整的细菌细胞，以及一些塑料切片和 X 射线数据集。 (C) 2007 Elsevier Inc. 保留所有权利。
We present a method for automatic full-precision alignment of the images in a tomographic tilt series. Full-precision automatic alignment of cryo electron microscopy images has remained a difficult challenge to date, due to the limited electron dose and low image contrast. These facts lead to poor signal to noise ratio (SNR) in the images, which causes automatic feature trackers to generate errors, even with high contrast gold particles as fiducial features. To enable fully automatic alignment for full-precision reconstructions, we frame the problem probabilistically as finding the most likely particle tracks given a set of noisy images, using contextual information to make the solution more robust to the noise in each image. To solve this maximum likelihood problem, we use Markov Random Fields (MRF) to establish the correspondence of features in alignment and robust optimization for projection model estimation. The resulting algorithm, called Robust Alignment and Projection Estimation for Tomographic Reconstruction, or RAPTOR, has not needed any manual intervention for the difficult datasets we have tried, and has provided sub-pixel alignment that is as good as the manual approach by an expert user. We are able to automatically map complete and partial marker trajectories and thus obtain highly accurate image alignment. Our method has been applied to challenging cryo electron tomographic datasets with low SNR from intact bacterial cells, as well as several plastic section and X-ray datasets. (C) 2007 Elsevier Inc. All rights reserved.