Please use this identifier to cite or link to this item: https://doi.org/10.1117/12.853474
Title: MRF based joint registration and segmentation of dynamic renal MR images
Authors: Mahapatra, D.
Sun, Y. 
Keywords: MRFs
Perfusion MRI
Registration
Saliency
Segmentation labels
Issue Date: 2010
Source: Mahapatra, D., Sun, Y. (2010). MRF based joint registration and segmentation of dynamic renal MR images. Proceedings of SPIE - The International Society for Optical Engineering 7546 : -. ScholarBank@NUS Repository. https://doi.org/10.1117/12.853474
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. © 2010 Copyright SPIE - The International Society for Optical Engineering.
Source Title: Proceedings of SPIE - The International Society for Optical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/51215
ISBN: 9780819479426
ISSN: 0277786X
DOI: 10.1117/12.853474
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