Thumbnail
Access Restriction
Open

Author Bai, Bing ♦ Kantor, Paul ♦ Shokoufandeh, Ali ♦ Silver, Deborah
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Ica Component ♦ Similarity Measurement ♦ Novel Similarity Measure ♦ Large Collection ♦ Brain Image ♦ Mod-erate Size Fmri Image Database ♦ Ica-based Component Selection ♦ Probabilistic Independent Compo-nent Analysis ♦ High Level Task-related Feature ♦ Corresponding Activation Map ♦ Fmri Dataset ♦ Retrieval System ♦ Fmri Brain Image ♦ Component-wise Similarity ♦ Fmri Brain Image Retrieval ♦ Direct Correlation ♦ Activated Re-gions ♦ Brain Ex-periments ♦ Maximum Weight Bipartite Match-ing ♦ Fmri Image Retrieval ♦ Similar Task ♦ Time Course Spectrum ♦ Considerable Vari-ety ♦ Time Series ♦ Cognitive Process
Description 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 ex-periments. 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 us-ing a number of ICA components as high level task-related features. The similarity between two datasets is the value of the maximum weight bipartite match-ing 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 outper-forms 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.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2007-01-01
Publisher Institution EIGHTH MEXICAN INTERNATIONAL CONFERENCE ON CURRENT TRENDS IN COMPUTER SCIENCE (ENC