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Spatiotemporal Denoising and Clustering of fMRI Data
This paper examines combined spatiotemporal denoising and clustering of functional magnetic resonance imaging (fMRI) time series. Most fMRI denoising methods are implemented either in spatial or temporal domain without taking into account both space and time information. In this work, a spatiotempor...
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| creator | Song, X. Murphy, M. Wyrwicz, A. M. |
| description | This paper examines combined spatiotemporal denoising and clustering of functional magnetic resonance imaging (fMRI) time series. Most fMRI denoising methods are implemented either in spatial or temporal domain without taking into account both space and time information. In this work, a spatiotemporal denoising method is developed where spatial denoising is implemented by Bayesian shrinkage that uses temporal prior information obtained by statistical testing on all voxel time courses. After the denoising, a set of spatiotemporal features are extracted and characterized by a Gaussian mixture model, which is applied to detect activated areas. The proposed methods have been tested on both synthetic and experimental data, and the results demonstrate their effectiveness. |
| doi_str_mv | 10.1109/ICIP.2006.313025 |
| format | conference_proceeding |
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M.</creator><creatorcontrib>Song, X. ; Murphy, M. ; Wyrwicz, A. M.</creatorcontrib><description>This paper examines combined spatiotemporal denoising and clustering of functional magnetic resonance imaging (fMRI) time series. Most fMRI denoising methods are implemented either in spatial or temporal domain without taking into account both space and time information. In this work, a spatiotemporal denoising method is developed where spatial denoising is implemented by Bayesian shrinkage that uses temporal prior information obtained by statistical testing on all voxel time courses. After the denoising, a set of spatiotemporal features are extracted and characterized by a Gaussian mixture model, which is applied to detect activated areas. 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M.</creatorcontrib><title>Spatiotemporal Denoising and Clustering of fMRI Data</title><title>2006 International Conference on Image Processing</title><addtitle>ICIP</addtitle><description>This paper examines combined spatiotemporal denoising and clustering of functional magnetic resonance imaging (fMRI) time series. Most fMRI denoising methods are implemented either in spatial or temporal domain without taking into account both space and time information. In this work, a spatiotemporal denoising method is developed where spatial denoising is implemented by Bayesian shrinkage that uses temporal prior information obtained by statistical testing on all voxel time courses. After the denoising, a set of spatiotemporal features are extracted and characterized by a Gaussian mixture model, which is applied to detect activated areas. The proposed methods have been tested on both synthetic and experimental data, and the results demonstrate their effectiveness.</description><subject>Bayesian methods</subject><subject>Bayesian shrinkage</subject><subject>Brain</subject><subject>Feature extraction</subject><subject>Functional magnetic resonance imaging</subject><subject>Gaussian mixture model</subject><subject>Gaussian noise</subject><subject>Magnetic resonance imaging</subject><subject>Noise reduction</subject><subject>Parameter estimation</subject><subject>Spatiotemporal phenomena</subject><subject>Testing</subject><subject>wavelet</subject><subject>Wavelet coefficients</subject><issn>1522-4880</issn><issn>2381-8549</issn><isbn>9781424404803</isbn><isbn>1424404800</isbn><isbn>9781424404810</isbn><isbn>1424404819</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVj8tKxEAQRdsXGMfZC27yAxmrurv6sZTMqIERxcd66CTVEskkIYkL_15FN64uhwMHrhAXCCtE8FdFXjyuJIBZKVQg6UAsvXWopdagHcKhSKRymDnS_uifA3UsEiQpM-0cnIqzaXoHkIAKE6GfhzA3_cz7oR9Dm66565up6d7S0NVp3n5MM48_2Mc03j8V6TrM4VycxNBOvPzbhXi92bzkd9n24bbIr7dZg5bmzKuIbDGywgAcJDMZU0ZTy0ghVrqiUnsqqQRdKYyKyNf2-4QzFQFZUAtx-dttmHk3jM0-jJ87jWDRkPoCXMZIvQ</recordid><startdate>200610</startdate><enddate>200610</enddate><creator>Song, X.</creator><creator>Murphy, M.</creator><creator>Wyrwicz, A. M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>200610</creationdate><title>Spatiotemporal Denoising and Clustering of fMRI Data</title><author>Song, X. ; Murphy, M. ; Wyrwicz, A. M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-93f1e71fe31a0ea2ee566bf6d2f5afc4c5b495b5b04c31f3559d723886c505703</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Bayesian methods</topic><topic>Bayesian shrinkage</topic><topic>Brain</topic><topic>Feature extraction</topic><topic>Functional magnetic resonance imaging</topic><topic>Gaussian mixture model</topic><topic>Gaussian noise</topic><topic>Magnetic resonance imaging</topic><topic>Noise reduction</topic><topic>Parameter estimation</topic><topic>Spatiotemporal phenomena</topic><topic>Testing</topic><topic>wavelet</topic><topic>Wavelet coefficients</topic><toplevel>online_resources</toplevel><creatorcontrib>Song, X.</creatorcontrib><creatorcontrib>Murphy, M.</creatorcontrib><creatorcontrib>Wyrwicz, A. M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Song, X.</au><au>Murphy, M.</au><au>Wyrwicz, A. M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Spatiotemporal Denoising and Clustering of fMRI Data</atitle><btitle>2006 International Conference on Image Processing</btitle><stitle>ICIP</stitle><date>2006-10</date><risdate>2006</risdate><spage>2857</spage><epage>2860</epage><pages>2857-2860</pages><issn>1522-4880</issn><eissn>2381-8549</eissn><isbn>9781424404803</isbn><isbn>1424404800</isbn><eisbn>9781424404810</eisbn><eisbn>1424404819</eisbn><abstract>This paper examines combined spatiotemporal denoising and clustering of functional magnetic resonance imaging (fMRI) time series. Most fMRI denoising methods are implemented either in spatial or temporal domain without taking into account both space and time information. In this work, a spatiotemporal denoising method is developed where spatial denoising is implemented by Bayesian shrinkage that uses temporal prior information obtained by statistical testing on all voxel time courses. After the denoising, a set of spatiotemporal features are extracted and characterized by a Gaussian mixture model, which is applied to detect activated areas. The proposed methods have been tested on both synthetic and experimental data, and the results demonstrate their effectiveness.</abstract><pub>IEEE</pub><doi>10.1109/ICIP.2006.313025</doi><tpages>4</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1522-4880 |
| ispartof | 2006 International Conference on Image Processing, 2006, p.2857-2860 |
| issn | 1522-4880 2381-8549 |
| language | eng |
| recordid | cdi_ieee_primary_4107165 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bayesian methods Bayesian shrinkage Brain Feature extraction Functional magnetic resonance imaging Gaussian mixture model Gaussian noise Magnetic resonance imaging Noise reduction Parameter estimation Spatiotemporal phenomena Testing wavelet Wavelet coefficients |
| title | Spatiotemporal Denoising and Clustering of fMRI Data |
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