Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets
Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2012/347120 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850210800640196608 |
|---|---|
| author | Nicolas Robitaille Abderazzak Mouiha Burt Crépeault Fernando Valdivia Simon Duchesne The Alzheimer's Disease Neuroimaging Initiative |
| author_facet | Nicolas Robitaille Abderazzak Mouiha Burt Crépeault Fernando Valdivia Simon Duchesne The Alzheimer's Disease Neuroimaging Initiative |
| author_sort | Nicolas Robitaille |
| collection | DOAJ |
| description | Intensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image. |
| format | Article |
| id | doaj-art-8de9abfebabc4416909dcdf37d0d2f64 |
| institution | OA Journals |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-8de9abfebabc4416909dcdf37d0d2f642025-08-20T02:09:41ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962012-01-01201210.1155/2012/347120347120Tissue-Based MRI Intensity Standardization: Application to Multicentric DatasetsNicolas Robitaille0Abderazzak Mouiha1Burt Crépeault2Fernando Valdivia3Simon Duchesne4The Alzheimer's Disease Neuroimaging Initiative5Centre de Recherche de l’Institut Universitaire en Santé Mentale de Québec, 2601 Chemin de la Canardière, Québec, QC, G1J 2G3, CanadaCentre de Recherche de l’Institut Universitaire en Santé Mentale de Québec, 2601 Chemin de la Canardière, Québec, QC, G1J 2G3, CanadaCentre de Recherche de l’Institut Universitaire en Santé Mentale de Québec, 2601 Chemin de la Canardière, Québec, QC, G1J 2G3, CanadaCentre de Recherche de l’Institut Universitaire en Santé Mentale de Québec, 2601 Chemin de la Canardière, Québec, QC, G1J 2G3, CanadaCentre de Recherche de l’Institut Universitaire en Santé Mentale de Québec, 2601 Chemin de la Canardière, Québec, QC, G1J 2G3, CanadaAlzheimer's Disease Neuroimaging Initiative, 4150 Clement Street, Building 13 (114M), San Francisco, CA 94121, USAIntensity standardization in MRI aims at correcting scanner-dependent intensity variations. Existing simple and robust techniques aim at matching the input image histogram onto a standard, while we think that standardization should aim at matching spatially corresponding tissue intensities. In this study, we present a novel automatic technique, called STI for STandardization of Intensities, which not only shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information. STI uses joint intensity histograms to determine intensity correspondence in each tissue between the input and standard images. We compared STI to an existing histogram-matching technique on two multicentric datasets, Pilot E-ADNI and ADNI, by measuring the intensity error with respect to the standard image after performing nonlinear registration. The Pilot E-ADNI dataset consisted in 3 subjects each scanned in 7 different sites. The ADNI dataset consisted in 795 subjects scanned in more than 50 different sites. STI was superior to the histogram-matching technique, showing significantly better intensity matching for the brain white matter with respect to the standard image.http://dx.doi.org/10.1155/2012/347120 |
| spellingShingle | Nicolas Robitaille Abderazzak Mouiha Burt Crépeault Fernando Valdivia Simon Duchesne The Alzheimer's Disease Neuroimaging Initiative Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets International Journal of Biomedical Imaging |
| title | Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets |
| title_full | Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets |
| title_fullStr | Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets |
| title_full_unstemmed | Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets |
| title_short | Tissue-Based MRI Intensity Standardization: Application to Multicentric Datasets |
| title_sort | tissue based mri intensity standardization application to multicentric datasets |
| url | http://dx.doi.org/10.1155/2012/347120 |
| work_keys_str_mv | AT nicolasrobitaille tissuebasedmriintensitystandardizationapplicationtomulticentricdatasets AT abderazzakmouiha tissuebasedmriintensitystandardizationapplicationtomulticentricdatasets AT burtcrepeault tissuebasedmriintensitystandardizationapplicationtomulticentricdatasets AT fernandovaldivia tissuebasedmriintensitystandardizationapplicationtomulticentricdatasets AT simonduchesne tissuebasedmriintensitystandardizationapplicationtomulticentricdatasets AT thealzheimersdiseaseneuroimaginginitiative tissuebasedmriintensitystandardizationapplicationtomulticentricdatasets |