Feature-Based Fusion of Medical Imaging Data
Feature-Based Fusion of Medical Imaging Data
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DOI：
10.1109/titb.2008.923773
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发表时间：
2009-09-01
影响因子：
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通讯作者：
Adali, Tuelay
中科院分区：
文献类型：
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作者：
Calhoun, Vince D.;Adali, Tuelay
针对给定研究采集多种脑成像类型是非常常见的做法。已经提出了许多用于组合或融合多任务或多模态信息的方法。这些可以大致分为那些试图研究多模态成像的收敛，例如，功能和结构如何在大脑的同一区域中相关，以及那些试图研究模态的互补性质，例如，利用时间EEG信息和空间功能磁共振成像信息。在这些类别中的每一个中，可以尝试数据集成（使用一种成像模态来改善另一种成像模态的结果）或真正的数据融合（其中利用多种模态来相互通知）。我们回顾了这两种方法，并提出了一个最近的计算方法，首先预处理的数据来计算感兴趣的功能。然后使用独立分量分析以多变量方式分析特征。我们详细描述了这种方法，并提供了它如何被用于不同的融合任务的例子。我们还提出了一种方法，用于选择哪种方式的组合提供了最大的价值，在歧视群体。最后，我们总结和描述未来的研究课题。
The acquisition of multiple brain imaging types for a given study is a very common practice. There have been a number of approaches proposed for combining or fusing multitask or multimodal information. These can be roughly divided into those that attempt to study convergence of multimodal imaging, for example, how function and structure are related in the same region of the brain, and those that attempt to study the complementary nature of modalities, for example, utilizing temporal EEG information and spatial functional magnetic resonance imaging information. Within each of these categories, one can attempt data integration (the use of one imaging modality to improve the results of another) or true data fusion (in which multiple modalities are utilized to inform one another). We review both approaches and present a recent computational approach that first preprocesses the data to compute features of interest. The features are then analyzed in a multivariate manner using independent component analysis. We describe the approach in detail and provide examples of how it has been used for different fusion tasks. We also propose a method for selecting which combination of modalities provides the greatest value in discriminating groups. Finally, we summarize and describe future research topics.