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Clustering techniques for neuroimaging applications.

WIREs Data Mining Knowl. Discov. 6, 22–36 (2015)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Clustering has been proven useful for knowledge discovery from massive data in many applications ranging from market segmentation to bioinformatics. In this study, we focus on clustering large amounts of medical image data of the human brain to identify structures of interest. Advanced Magnetic Resonance Imaging techniques enable unprecedented insights into the complex processes in the brain. However, especially for clinical studies, a huge amount of data has to be processed in order to find patterns characterizing the structure and function of the healthy brain and its alternations associated with diseases. We survey clustering methods specifically designed for neuroimaging applications such as segmentation of fiber tracks and lesions, as well as methods that can deal with multimodal imaging data. Furthermore, we will illustrate how clustering enables knowledge discovery from data by enhancing the performance of supervised techniques and discovering meaningful subgroups of subjects. The main purpose of this study is to give an introduction on how versatile clustering techniques can be applied in neuroimaging to tackle different applications where automated methods are desired.
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Publication type Article: Journal article
Document type Scientific Article
Reviewing status