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https://olympias.lib.uoi.gr/jspui/handle/123456789/10714Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Nikou, C. | en |
| dc.contributor.author | Bueno, G. | en |
| dc.contributor.author | Heitz, F. | en |
| dc.contributor.author | Armspach, J. P. | en |
| dc.date.accessioned | 2015-11-24T17:00:08Z | - |
| dc.date.available | 2015-11-24T17:00:08Z | - |
| dc.identifier.issn | 0278-0062 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/10714 | - |
| dc.rights | Default Licence | - |
| dc.subject | brain isolation | en |
| dc.subject | image registration | en |
| dc.subject | magnetic resonance imaging (mri) | en |
| dc.subject | physically based deformable model | en |
| dc.subject | single photon emission computed tomography (spect) | en |
| dc.subject | statistical shape models | en |
| dc.subject | mr-images | en |
| dc.subject | segmentation | en |
| dc.subject | deformations | en |
| dc.subject | registration | en |
| dc.subject | surfaces | en |
| dc.subject | atlas | en |
| dc.title | A joint physics-based statistical deformable model for multimodal brain image analysis | en |
| heal.type | journalArticle | - |
| heal.type.en | Journal article | en |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.language | en | - |
| heal.access | campus | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής | el |
| heal.publicationDate | 2001 | - |
| heal.abstract | A probabilistic deformable, model for the representation of multiple brain structures is described. The statistically learned deformable model! represents the relative location of different anatomical surfaces in brain magnetic resonance images (MRIs) and accommodates their significant variability across different individuals. The surfaces of each anatomical structure are parameterized by the amplitudes of the vibration modes of a deformable spherical mesh. For a given MRI in the training set, a vector containing the largest vibration modes describing the different deformable surfaces is created. This random vector is statistically constrained by retaining the most significant variation modes of its Karhunen-Loeve expansion on the training population. By these means, the conjunction of surfaces are deformed according to the anatomical variability observed in the training set. Two applications of the joint probabilistic deformable model are presented: isolation of the brain from MRI using the probabilistic constraints embedded in the model; and deformable model-based registration of three-dimensional multimodal (magnetic resonance/single photon emission computed tomography) brain images without removing nonbrain structures. The multiobject deformable model may be considered as a first step toward the development of a general purpose probabilistic anatomical atlas of the brain. | en |
| heal.journalName | IEEE Trans Med Imaging | en |
| heal.journalType | peer reviewed | - |
| heal.fullTextAvailability | TRUE | - |
| Appears in Collections: | Άρθρα σε επιστημονικά περιοδικά ( Ανοικτά) | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| nikou-2001-A joint physics-based statistical deformable model for multimodal brain image analysis.pdf | 289.24 kB | Adobe PDF | View/Open Request a copy |
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