HAMMER: Hierarchical attribute matching mechanism for elastic registration
HAMMER: Hierarchical attribute matching mechanism for elastic registration
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DOI：
10.1109/tmi.2002.803111
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发表时间：
2002-11-01
影响因子：
10.6
通讯作者：
Davatzikos, C
中科院分区：
文献类型：
--
作者：
Shen, DG;Davatzikos, C
提出了一种新方法，用于弹性注册医学图像，并应用于大脑的磁共振图像。实验结果表明，来自不同受试者的图像叠加的精度非常高。拟议算法中有两个主要的新颖性。首先，它使用属性矢量，即在图像中每个体素上定义的一组几何矩（GMI），并根据组织图计算出来，以在不同的尺度上反映基础解剖结构。属性向量（如果足够丰富）可以区分图像的不同部分，这有助于在变形过程中建立解剖学对应。通过减少潜在匹配的歧义，它还有助于减少局部最小值。这是我们方法的基本偏差，称为弹性配准（Hammer）的分层属性匹配机制（Hammer），远离其他体积变形方法，通常基于最大化图像相似性。其次，为了避免被局部最小值捕获，即次优差匹配，Hammer使用了连续的能量函数近似值，该能量函数被较低的尺寸光滑能量功能优化，这些函数的构建的局部最小值较少。这是通过层次选择具有不同属性向量的驱动特征来实现的，因此大大降低了查找对应关系的歧义。许多实验表明，所提出的算法导致来自具有显着解剖学差异的个体的图像数据准确叠加。
A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate very high accuracy in superposition of images from different subjects. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e., a set of geometric moment invariants (GMIs) that are defined on each voxel in an image and are calculated from the tissue maps, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure; it also helps reduce local minima, by reducing ambiguity in potential matches. This is a fundamental deviation of our method, referred to as the hierarchical attribute matching mechanism for elastic registration (HAMMER), from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, in order to avoid being trapped by local minima, i.e., suboptimal poor matches, HAMMER uses a successive approximation of the energy function being optimized by lower dimensional smooth energy functions, which are constructed to have significantly fewer local minima. This is achieved by hierarchically selecting the driving features that have distinct attribute vectors, thus, drastically reducing ambiguity in finding correspondence. A number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences.