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Proceedings Paper

MRF based joint registration and segmentation of dynamic renal MR images
Author(s): Dwarikanath Mahapatra; Ying Sun
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Paper Abstract

Joint registration and segmentation (JRS) is an effective approach to combine the complementary information of segmentation labels with registration parameters. While most such integrated approaches have been tested on static images, in this work we focus on JRS of dynamic image sequences. For dynamic contrast enhanced images, previous works have focused on multi-stage approaches that interleave registration and segmentation. We propose a Markov random field (MRF) based solution which uses saliency, intensity, edge orientation and segmentation labels for JRS of renal perfusion images. An expectation-maximization (EM) framework is used where the entire image sequence is first registered followed by updating the segmentation labels. Experiments on real patient datasets exhibiting elastic deformations demonstrate the effectiveness of our MRF-based JRS approach.

Paper Details

Date Published: 26 February 2010
PDF: 6 pages
Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754617 (26 February 2010); doi: 10.1117/12.853474
Show Author Affiliations
Dwarikanath Mahapatra, National Univ. Singapore (Singapore)
Ying Sun, National Univ. Singapore (Singapore)


Published in SPIE Proceedings Vol. 7546:
Second International Conference on Digital Image Processing
Kamaruzaman Jusoff; Yi Xie, Editor(s)

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