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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/596
DC FieldValueLanguage
dc.contributor.authorLe Dinh Anen_US
dc.contributor.authorPham Quoc Baoen_US
dc.date.accessioned2020-03-12T17:04:55Z-
dc.date.available2020-03-12T17:04:55Z-
dc.date.issued2017-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/596-
dc.description.abstractThe study of functional regions in human brain has started since Renaissance era, However, only until recent years, with developments of many medical imaging technique, does the study of the complex brain functions continue and develop. MRI is one of these medical imaging technique, which not only can provide images with high resolution, but also allows collection of many different kind of medical images. Many scientists use MRI scanners to investigate changes in brain function, which is called functional magnetic resonance imaging OMR° technique. Moreover, many different approaches have also been proposed to analyze fMRI data,, one of which is the famous Independent Component Analysis (ICA) algorithm. However, with recent emergence of compressive sensing technique and sparse representation in signal processing field, a new approach, MCA-KSVD, is proposed. In this report, the concept of how a MRI scanner works and the idea of MCA-KSVI) algorithm are mainly discussed. The implementation of the proposed algorithm is also presented in this report.en_US
dc.language.isoenen_US
dc.publisherVietnamese-German Universityen_US
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectBrain network detectionen_US
dc.subjectMorphological component analysisen_US
dc.subjectMRIen_US
dc.titleBrain network detection using morphological component analysis for functional MRIen_US
dc.typeThesisen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Electrical Engineering and Information Technology (EEIT)
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