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|Title:||MRF based joint registration and segmentation of dynamic renal MR images|
|Citation:||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|
|Appears in Collections:||Staff Publications|
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