1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 6qtX3pFwXQZG2LgkFdY/UQ4Vi |
Repository | sid.inpe.br/sibgrapi@80/2008/07.21.15.49 |
Last Update | 2008:07.31.13.53.06 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi@80/2008/07.21.15.49.25 |
Metadata Last Update | 2022:06.14.00.13.50 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2008.5 |
Citation Key | OlivaIsoaMato:2008:BaEsHy |
Title | Bayesian estimation of Hyperparameters in MRI through the Maximum Evidence Method |
Format | Printed, On-line. |
Year | 2008 |
Access Date | 2024, May 02 |
Number of Files | 1 |
Size | 346 KiB |
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2. Context | |
Author | 1 Oliva, Damián Ernesto 2 Isoardi, Roberto Andrés 3 Mato, Germán |
Affiliation | 1 Universidad Nacional de Buenos Aires, Argentina 2 Escuela de Medicina Nuclear, Mendoza, Argentina 3 Grupo Física Estadística, Centro Atómico Bariloche, Argentina |
Editor | Jung, Cláudio Rosito Walter, Marcelo |
Conference Name | Brazilian Symposium on Computer Graphics and Image Processing, 21 (SIBGRAPI) |
Conference Location | Campo Grande, MS, Brazil |
Date | 12-15 Oct. 2008 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2008-07-31 13:53:06 :: risoardi@ieee.org -> administrator :: 2009-08-13 20:39:00 :: administrator -> risoardi@ieee.org :: 2010-08-28 20:03:23 :: risoardi@ieee.org -> administrator :: 2022-06-14 00:13:50 :: administrator -> :: 2008 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | Image segmentation Bayesian analysis MRI |
Abstract | Bayesian inference methods are commonly applied to the classification of brain Magnetic Resonance images (MRI). We use the Maximum Evidence (ME) approach to estimate the most probable parameters and hyperparameters for models that take into account discrete classes (DM) and models accounting for the partial volume effect (PVM). An approximate algorithm was developed for model optimization, since the exact image inference calculation is computationally expensive. The method was validated using simulated images and a digital phantom. We show that the Evidence is a very useful figure for error prediction, which is to be maximized respect to the hyperparameters. Additionally, it provides a tool to determine the most probable model given measured data. |
Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2008 > Bayesian estimation of... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Bayesian estimation of... |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | there are no files |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/UQ4Vi |
zipped data URL | http://urlib.net/zip/6qtX3pFwXQZG2LgkFdY/UQ4Vi |
Language | en |
Target File | Oliva-Bayesian.pdf |
User Group | risoardi@ieee.org administrator |
Visibility | shown |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/46SG4TH 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2022/05.14.04.55 2 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage e-mailaddress edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume |
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