Robust Computation of Mutual Information Using Spatially Adaptive Meshes
Robust Computation of Mutual Information Using Spatially Adaptive Meshes
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DOI：
10.1007/978-3-540-75757-3_115
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发表时间：
2007-10
期刊：
影响因子：
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通讯作者：
H. Sundar;D. Shen;G. Biros;Chenyang Xu-;C. Davatzikos
中科院分区：
文献类型：
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作者：
H. Sundar;D. Shen;G. Biros;Chenyang Xu-;C. Davatzikos
我们提出了一种新的方法，快速和强大的计算信息论相似性措施对齐的多模态医学图像。所提出的方法定义了一个非均匀的，自适应的采样方案估计的熵的图像，这是不容易局部最大值相比，均匀和随机采样。采样是使用模板图像的八叉树分区定义的，并且比其他提出的非均匀采样方法更优选，因为它尊重底层数据分布。它还自然地扩展到多分辨率配准方法，该方法通常用于医学图像的对准。使用从BrainWeb数据库获得的模拟MR图像和临床CT和SPECT图像证明了所提出的方法的有效性。
We present a new method for the fast and robust computation of information theoretic similarity measures for alignment of multi-modality medical images. The proposed method defines a non-uniform, adaptive sampling scheme for estimating the entropies of the images, which is less vulnerable to local maxima as compared to uniform and random sampling. The sampling is defined using an octree partition of the template image, and is preferable over other proposed methods of non-uniform sampling since it respects the underlying data distribution. It also extends naturally to a multi-resolution registration approach, which is commonly employed in the alignment of medical images. The effectiveness of the proposed method is demonstrated using both simulated MR images obtained from the BrainWeb database and clinical CT and SPECT images.