Edge intensity normalization as a bias field correction during balloon snake segmentation of breast MRI.
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.
Hill
Andrew
A
School of Information Technology and Electrical Engineering, The University of Queensland, Qld, Australia. ahill@itee.uq.edu.au
Mehnert
Andrew
A
Crozier
Stuart
S
D000388:Air; D000465:Algorithms; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D001330:Electronic Data Processing; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography / Q000379:methods; D015588:Observer Variation; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012867:Skin / Q000473:pathology; D013382:Subtraction Technique