Texture-Based Simultaneous Registration and Segmentation of Breast DCE-MRI

Authors:
Yang Can Gong;Michael Brady
Affiliations:
Wolfson Medical Vision Lab, University of Oxford, UK;Wolfson Medical Vision Lab, University of Oxford, UK
Venue:
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
Year:
2008

Citing 3
Cited 0

Hidden Markov Measure Field Models for Image Segmentation

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Texture based mammogram classification and segmentation

IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Simultaneous segmentation and registration of contrast-enhanced breast MRI

IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging

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Abstract

We present a registration method for breast dynamic contrast-enhanced(DCE) MRI data based on texture information. The algorithm combines feature and spatial information to propose an image segmentation based on a Hidden Markov Random Measure Field(HMRMF) model using expectation-maximisation(EM) iteration. It can be used to simultaneously estimate parameters in order to segment and register the images iteratively. Global motions are modeled by an affine transformation, while local breast motions are described using free-form deformations(FFD) based on B-splines. Experimental results on real DCE-MRI data are presented to demonstrate the performance of the algorithm.