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Clustering techniques for neuroimaging applications
Author(s) -
Derntl Alexandra,
Plant Claudia
Publication year - 2015
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.1174
Subject(s) - cluster analysis , neuroimaging , computer science , segmentation , consensus clustering , market segmentation , artificial intelligence , clustering high dimensional data , knowledge extraction , data mining , data science , machine learning , medical imaging , correlation clustering , cure data clustering algorithm , neuroscience , psychology , marketing , business
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. WIREs Data Mining Knowl Discov 2016, 6:22–36. doi: 10.1002/widm.1174 This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Health Care Technologies > Structure Discovery and Clustering