Paper Info
Title
Automatic segmentation of newborns' skull and fontanel from CT data using model-based variational level set.
Abstract
The newborn’s cranium is composed of flat cranial bone and fontanels forming together the envelope of the cerebral cavity. The fontanels are relatively flexible since they consist of fibrous membrane that ossifies during maturation becoming flat cranial bone as well. Fontanels give less contrast in computerized tomography (CT) images; they can be identified as gaps between the cranial bones. In this paper, we propose an automatic model-based method using variational level set to segment the skull and fontanels from CT images. In this approach, firstly a skull model consisting of cranial bones and fontanels is created and then used as constraint for level set evolution. Then, by removing the cranial bones from the segmented skulls, the fontanels are obtained. To verify the validity of the achieved results, automatically segmented skull and fontanels have been compared with the ones manually segmented by an expert using Dice similarity and Hausdorff dissimilarity measures, which show the good agreement between them. Furthermore, the surface areas of cranium and fontanel have been determined for these segmentations. The results for both, manual and automatic segmentation, are in good agreement.
Year
DOI
Venue
2014
10.1007/s11760-012-0300-x
Signal, Image and Video Processing
Keywords
Field
DocType
Newborn, CT image, Skull, Fontanel, Segmentation
Fibrous membrane,Computer vision,Pattern recognition,Segmentation,Level set,Tomography,Cranial bone,Artificial intelligence,Skull,Mathematics
Journal
Volume
Issue
ISSN
8
2
1863-1711
Citations 
PageRank 
References 
6
0.57
11
Authors
8
Name
Order
Citations
PageRank
Nassim Jafarian160.91
Kamran Kazemi28112.24
Hamid Abrishami Moghaddam321922.96
Reinhard Grebe420441.00
Marc Fournier571.26
Mohammad Sadegh Helfroush67011.30
Catherine Gondry-Jouet7302.37
Fabrice Wallois8485.76