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Cerebellum segmentation in MRI using atlas registration and local multi-scale image descriptors

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

We propose a novel cerebellum segmentation method for MRI, based on a combination of statistical models of the structure's expected location in the brain and its local appearance. The appearance model is obtained from a k-nearest-neighbor classifier, which uses a set of multi-scale local image descriptors as features. The spatial model is constructed by registering multiple manually annotated datasets to the unlabeled target image. The two components are then combined in a Bayesian framework. The method is quantitatively validated in a leave-one-out experiment using 18 MR images of elderly subjects. The experiment showed that the method produces accurate segmentations. The mean Dice similarity index compared to the manual reference was 0.953 for left and right, and the mean surface distance was 0.49 mm for left and 0.50 mm for right. The combined atlas- and appearance-based method was found to be more accurate than a method based on atlas-registration alone.
OriginalsprogEngelsk
TitelIEEE International Symposium on Biomedical Imaging (ISBI'09) : From Nano to Macro
Antal sider4
Publikationsdato2009
Sider221-224
ISBN (Trykt)978-1-4244-3931-7
DOI
StatusUdgivet - 2009
BegivenhedISBI 2009, 6th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Boston, Massachusetts, USA
Varighed: 28 jun. 00091 jul. 0009
Konferencens nummer: 6

Konference

KonferenceISBI 2009, 6th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Nummer6
LandUSA
ByBoston, Massachusetts
Periode28/06/000901/07/0009
NavnUden navn
ISSN1945-7928

ID: 14307614