Edge intensity normalization as a bias field correction during balloon snake segmentation of breast MRI

Hill, A., Mehnert, A. and Crozier, S. (2008). Edge intensity normalization as a bias field correction during balloon snake segmentation of breast MRI. In: G. Dumont and H. Galiana, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2008. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vancouver Canada, (3040-3043). 20-24 August 2008. doi:10.1109/IEMBS.2008.4649844


Author Hill, A.
Mehnert, A.
Crozier, S.
Title of paper Edge intensity normalization as a bias field correction during balloon snake segmentation of breast MRI
Conference name Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Conference location Vancouver Canada
Conference dates 20-24 August 2008
Proceedings title 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2008   Check publisher's open access policy
Journal name 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vols 1-8   Check publisher's open access policy
Place of Publication United States
Publisher IEEE
Publication Year 2008
Sub-type Fully published paper
DOI 10.1109/IEMBS.2008.4649844
ISBN 9781424418145
ISSN 1557-170X
Editor G. Dumont
H. Galiana
Start page 3040
End page 3043
Total pages 4
Collection year 2009
Language eng
Formatted Abstract/Summary
Segmentation of fat suppressed dynamic contrast enhanced MRI (DCE-MRI) image data can pose significant problems because of the inherently poor signal-to-noise ratio (SNR) and intensity variations due to the bias field. Segmentation methods such as balloon snakes, while able to operate in a poor SNR environment, are sensitive to variations in edge intensity, which are regularly encountered within DCE-MRI due to the bias field. In order to overcome the effects of the bias field, an intensity normalization based on the strength of the strongest edge, i.e. the skin-air-boundary, is proposed and evaluated. This normalization allows balloon segmentations to be run three times faster while maintaining, or even improving accuracy.
Subjects E1
090302 Biomechanical Engineering
920102 Cancer and Related Disorders
Keyword Image
Segmentation
Algorithms
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Fri, 17 Apr 2009, 20:36:49 EST by Donna Clark on behalf of School of Information Technol and Elec Engineering