Brain MRI T1-Map and T1-weighted image segmentation in a variational framework

Authors:
Ping-Feng Chen;R. Grant Steen;Anthony Yezzi;Hamid Krim
Affiliations:
North Carolina State University, Department of Electrical and Computer Engineering, USA;Medical Communications Consultants, LLC, USA;Georgia Institute of Technology, School of Electrical and Computer Engineering, USA;North Carolina State University, Department of Electrical and Computer Engineering, USA
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

Citing 0
Cited 1

Bayesian classification using DCT features for brain tumor detection

KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we propose a constrained version of Mumford-Shah's[1] segmentationwith an information-theoretic point of view[2] in order to devise a systematic procedure to segment brain MRI data for two modalities of parametric T1-Map and T1-weighted images in both 2-D and 3-D settings. The incorporation of a tuning weight in particular adds a probabilistic flavor to our segmentation method, and makes the three-tissue segmentation possible. Our method uses region based active contours which have proven to be robust. The method is validated by two real objects which were used to generate T1-Maps and also by two simulated brains of T1-weighted data from the BrainWeb[3] public database.