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Fully automatic brain segmentation using model-guided level sets and skeleton-based models
Linköping University, Sweden.ORCID iD: 0000-0002-0442-3524
Linköping University, Sweden.ORCID iD: 0000-0002-7750-1917
2013 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.

Place, publisher, year, edition, pages
2013.
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-258852OAI: oai:DiVA.org:kth-258852DiVA, id: diva2:1350260
Conference
MICCAI Grand Challenge on MR Brain Image Segmentation (MRBrainS’13)
Note

QC 20190917

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-17Bibliographically approved

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Smedby, Örjan

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Wang, ChunliangSmedby, Örjan
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