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Unsupervised Feature Extraction of in vivo Magnetic Resonance Spectra of Brain Tumours Using Independent Component Analysis
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posted on 2023-06-08, 10:37 authored by Christophe Ladroue, Anne Rosemary Tate, F A Howe, John R GriffithsWe present a method for automatically decomposing magnetic resonance (MR) spectra of different types of human brain tumours into components which directly reflect their different chemical compositions. The automatic analysis of in vivo MR spectra can be problematic due to their large dimensionality and the low signal to noise ratio. Principal Component Analysis allows an economic representation of the data but the extracted components themselves may bear little relationship to the underlying metabolites represented by the spectra. The Principal Components can be rotated in order to make them more meaningful but this requires expertise to decide on the transformation. In this study, we use Independent Component Analysis and show that this technique can overcome these two drawbacks and provide meaningful and representative components without requiring prior knowledge
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Publication status
- Published
Publisher URL
Pages
0.0Presentation Type
- paper
Event name
THird International Conference on Intelligent Data Engineering and Automated LearningEvent type
conferenceISBN
3-540-44025-9Full text available
- No
Peer reviewed?
- Yes