Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts.
In this paper, we propose a novel technique for skull stripping of infant (neonatal) brain magnetic resonance images using prior shape information within a graph cut framework. Skull stripping plays an important role in brain image analysis and is a major challenge for neonatal brain images. Popular...
| Publicado en: | Journal of Digital Imaging Vol. 25; no. 6; pp. 802 - 815 |
|---|---|
| Autor principal: | |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2012
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104432757&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104432757 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2012 vid: 25 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104432757 83184800 10.1007/s10278-012-9460-z NLM22354704 104432757 ppf: 802 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Skull Stripping of Neonatal Brain MRI: Using Prior Shape Information with Graph Cuts. aug: au: Mahapatra, Dwarikanath affil: Department of Computer Science, Swiss Federal Institute of Technology (ETH), Room CAB F 61.1, Universitätstrasse 6 8092 Zurich Switzerland sug: subj: Skull Radiography Brain Radiography Radiographic Image Enhancement Methods Algorithms Automation False Positive Results Comparative Studies Sensitivity and Specificity T-Tests P-Value Infant, Newborn Human Infant, Newborn: birth-1 month ab: In this paper, we propose a novel technique for skull stripping of infant (neonatal) brain magnetic resonance images using prior shape information within a graph cut framework. Skull stripping plays an important role in brain image analysis and is a major challenge for neonatal brain images. Popular methods like the brain surface extractor (BSE) and brain extraction tool (BET) do not produce satisfactory results for neonatal images due to poor tissue contrast, weak boundaries between brain and non-brain regions, and low spatial resolution. Inclusion of prior shape information helps in accurate identification of brain and non-brain tissues. Prior shape information is obtained from a set of labeled training images. The probability of a pixel belonging to the brain is obtained from the prior shape mask and included in the penalty term of the cost function. An extra smoothness term is based on gradient information that helps identify the weak boundaries between the brain and non-brain region. Experimental results on real neonatal brain images show that compared to BET, BSE, and other methods, our method achieves superior segmentation performance for neonatal brain images and comparable performance for adult brain images. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|