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Method for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error

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dc.affiliation.dptoUC3M. Departamento de Bioingenieríaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Biomedical Imaging and Instrumentation Groupes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: BSEL - Laboratorio de Ciencia e Ingeniería Biomédicaes
dc.contributor.authorGispert, Juan D.
dc.contributor.authorReig, Santiago
dc.contributor.authorPascau González-Garzón, Javier
dc.contributor.authorVaquero López, Juan José
dc.contributor.authorGarcía Barreno, Pedro
dc.contributor.authorDesco Menéndez, Manuel
dc.date.accessioned2010-10-07T10:46:25Z
dc.date.available2010-10-07T10:46:25Z
dc.date.issued2004-03
dc.description.abstractThis work presents a new algorithm (nonuniform intensity correction; NIC) for correction of intensity inhomogeneities in T1-weighted magnetic resonance (MR) images. The bias field and a bias-free image are obtained through an iterative process that uses brain tissue segmentation. The algorithm was validated by means of realistic phantom images and a set of 24 real images. The first evaluation phase was based on a public domain phantom dataset, used previously to assess bias field correction algorithms. NIC performed similar to previously described methods in removing the bias field from phantom images, without introduction of degradation in the absence of intensity inhomogeneity. The real image dataset was used to compare the performance of this new algorithm to that of other widely used methods (N3, SPM'99, and SPM2). This dataset included both low and high bias field images from two different MR scanners of low (0.5 T) and medium (1.5 T) static fields. Using standard quality criteria for determining the goodness of the different methods, NIC achieved the best results, correcting the images of the real MR dataset, enabling its systematic use in images from both low and medium static field MR scanners. A limitation of our method is that it might fail if the bias field is so high that the initial histogram does not show bimodal distribution for white and gray matter
dc.description.statusPublicado
dc.format.mimetypeapplication/pdf
dc.identifier.bibliographicCitationHuman Brain Mapping, 2004, vol. 22, n. 2, p. 133–144
dc.identifier.doi10.1002/hbm.20013
dc.identifier.issn1097-0193
dc.identifier.publicationfirstpage133
dc.identifier.publicationissue2
dc.identifier.publicationlastpage144
dc.identifier.publicationtitleHuman brain mapping
dc.identifier.publicationvolume22
dc.identifier.urihttps://hdl.handle.net/10016/9373
dc.language.isoeng
dc.publisherWiley
dc.relation.publisherversionhttp://dx.doi.org/10.1002/hbm.20013
dc.rights©Wiley
dc.rights.accessRightsopen access
dc.subject.ecienciaBiología y Biomedicina
dc.subject.otherNonuniform intensity correction
dc.subject.otherNIC
dc.subject.otherMagnetic resonance imaging
dc.subject.otherBias field
dc.subject.otherIntensity inhomogeneities
dc.subject.otherSegmentation algorithms
dc.titleMethod for bias field correction of brain T1-weighted magnetic resonance images minimizing segmentation error
dc.typeresearch article*
dc.type.reviewPeerReviewed
dspace.entity.typePublication
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