Measurement of brain structures based on statistical and geometrical 3D segmentation
In this paper we present a novel method for three-dimensional segmentation and measurement of volumetric data based on the combination of statistical and geometrical information. We represent the shape of complex three-dimensional structures, such as the cortex by combining a discrete 3D simplex mes...
| Main Authors: | , , , |
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| Format: | Conference item |
| Language: | English |
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Springer
2006
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| _version_ | 1824458938089734144 |
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| author | ángel, M Ballester, G Zisserman, A Brady, M |
| author_facet | ángel, M Ballester, G Zisserman, A Brady, M |
| author_sort | ángel, M |
| collection | OXFORD |
| description | In this paper we present a novel method for three-dimensional segmentation and measurement of volumetric data based on the combination of statistical and geometrical information. We represent the shape of complex three-dimensional structures, such as the cortex by combining a discrete 3D simplex mesh with the construction of a smooth surface using triangular Gregory-Bézier patches. A Gaussian model for the tissues present in the image is adopted, and a classification procedure which also estimates and corrects for the bias field present in the MRI is used. Confidence bounds are produced for all the measurements, thus obtaining bounds on the position of the surface segmenting the image. Performance is illustrated on multiple sclerosis phantom data and on real data. |
| first_indexed | 2025-02-19T04:33:50Z |
| format | Conference item |
| id | oxford-uuid:dab61200-8a12-48d4-92bd-9abca8515914 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:33:50Z |
| publishDate | 2006 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:dab61200-8a12-48d4-92bd-9abca85159142025-01-14T11:52:49ZMeasurement of brain structures based on statistical and geometrical 3D segmentationConference itemhttp://purl.org/coar/resource_type/c_5794uuid:dab61200-8a12-48d4-92bd-9abca8515914EnglishSymplectic ElementsSpringer2006ángel, MBallester, GZisserman, ABrady, MIn this paper we present a novel method for three-dimensional segmentation and measurement of volumetric data based on the combination of statistical and geometrical information. We represent the shape of complex three-dimensional structures, such as the cortex by combining a discrete 3D simplex mesh with the construction of a smooth surface using triangular Gregory-Bézier patches. A Gaussian model for the tissues present in the image is adopted, and a classification procedure which also estimates and corrects for the bias field present in the MRI is used. Confidence bounds are produced for all the measurements, thus obtaining bounds on the position of the surface segmenting the image. Performance is illustrated on multiple sclerosis phantom data and on real data. |
| spellingShingle | ángel, M Ballester, G Zisserman, A Brady, M Measurement of brain structures based on statistical and geometrical 3D segmentation |
| title | Measurement of brain structures based on statistical and geometrical 3D segmentation |
| title_full | Measurement of brain structures based on statistical and geometrical 3D segmentation |
| title_fullStr | Measurement of brain structures based on statistical and geometrical 3D segmentation |
| title_full_unstemmed | Measurement of brain structures based on statistical and geometrical 3D segmentation |
| title_short | Measurement of brain structures based on statistical and geometrical 3D segmentation |
| title_sort | measurement of brain structures based on statistical and geometrical 3d segmentation |
| work_keys_str_mv | AT angelm measurementofbrainstructuresbasedonstatisticalandgeometrical3dsegmentation AT ballesterg measurementofbrainstructuresbasedonstatisticalandgeometrical3dsegmentation AT zissermana measurementofbrainstructuresbasedonstatisticalandgeometrical3dsegmentation AT bradym measurementofbrainstructuresbasedonstatisticalandgeometrical3dsegmentation |