Multimodal Medical Volumetric Data Fusion Using 3-D Discrete Shearlet Transform and Global-to-Local Rule
Multimodal Medical Volumetric Data Fusion Using 3-D Discrete Shearlet Transform and Global-to-Local Rule
复制标题
使用 3-D 离散剪切波变换和全局到局部规则的多模态医学体积数据融合
DOI：
10.1109/tbme.2013.2279301
复制
发表时间：
2014-01-01
影响因子：
4.6
通讯作者：
Tian, Lianfang
中科院分区：
文献类型：
--
作者：
Wang, Lei;Li, Bin;Tian, Lianfang
传统的二维图像融合框架往往会丢失三维的层间信息。例如，三维（3-D）MRI切片的融合必须不仅考虑给定切片内的信息，而且考虑相邻切片内的信息。本文提出了一种基于三维剪切波空间的融合方法。另一方面，普遍使用的平均最大值融合规则只能捕获局部信息，而不能捕获任何全局信息，因为它是在局部窗口区域中实现的。因此，提出了一种全局到局部的融合规则。我们首先证明了高通子带的三维剪切波系数是高度非高斯的。然后，我们证明了这种重尾现象可以用广义高斯密度（GGD）来描述，两个子带之间的全局信息可以用两个GGD的Kullback-Leibler距离（KLD）来描述。根据KLD的非对称性，可以选择最终融合的全局信息。对合成数据和真实的数据的融合实验表明，该方法能获得较好的融合效果。
Traditional two-dimensional (2-D) fusion framework usually suffers from the loss of the between-slice information of the third dimension. For example, the fusion of three-dimensional (3-D) MRI slices must account for the information not only within the given slice but also the adjacent slices. In this paper, a fusion method is developed in 3-D shearlet space to overcome the drawback. On the other hand, the popularly used average-maximum fusion rule can capture only the local information but not any of the global information for it is implemented in a local window region. Thus, a global-to-local fusion rule is proposed. We firstly show the 3-D shearlet coefficients of the high-pass subbands are highly non-Gaussian. Then, we show this heavy-tailed phenomenon can be modeled by the generalized Gaussian density (GGD) and the global information between two subbands can be described by the Kullback-Leibler distance (KLD) of two GGDs. The finally fused global information can be selected according to the asymmetry of the KLD. Experiments on synthetic data and real data demonstrate that better fusion results can be obtained by the proposed method.