Conference proceeding
fMRI Brain Image Retrieval Based on ICA Components
Eighth Mexican International Conference on Current Trends in Computer Science (ENC 2007), pp 10-17
Sep 2007
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This manuscript proposes a retrieval system for fMRI brain images. Our goal is to find a similarity-metric to enable us to support queries for "similar tasks" for retrieval on a large collection of brain experiments. The system uses a novel similarity measure between the result of probabilistic independent component analysis (PICA) of brain images. Specifically, the times series of an fMRI dataset will be represented using a number of ICA components as high level task- related features. The similarity between two datasets is the value of the maximum weight bipartite matching defined on the component-wise similarities. The component-wise similarities are calculated based on the size of the overlap between the "highly activated" regions in the corresponding activation maps. We evaluated the performance of the proposed method on a moderate size fMRI image database with considerable variety. The ICA-based component selection in combination with bipartite matching similarity measure outperforms several other component selection methods and similarity measurements. The results also suggest that there is a direct correlation between the involvement of ICA components in cognitive processes and their time course spectrum. Along with other heuristics, this property can be for fMRI image retrieval and classification.
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Details
- Title
- fMRI Brain Image Retrieval Based on ICA Components
- Creators
- Bing Bai - Rutgers University, USAPaul Kantor - Rutgers University, USAAli Shokoufandeh - Drexel UniversityDeborah Silver - Rutgers University, USAIEEE Comp Soc
- Publication Details
- Eighth Mexican International Conference on Current Trends in Computer Science (ENC 2007), pp 10-17
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000251389800002
- Scopus ID
- 2-s2.0-47849107011
- Other Identifier
- 991019168480204721
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Engineering, Electrical & Electronic