MRF based joint registration and segmentation of dynamic renal MR images

Dwarikanath Mahapatra; Ying Sun

doi:10.1117/12.853474

26 February 2010 MRF based joint registration and segmentation of dynamic renal MR images

Dwarikanath Mahapatra, Ying Sun

Proceedings Volume 7546, Second International Conference on Digital Image Processing; 754617 (2010) https://doi.org/10.1117/12.853474
Event: Second International Conference on Digital Image Processing, 2010, Singapore, Singapore

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.

Citation Download Citation

Dwarikanath Mahapatra and Ying Sun "MRF based joint registration and segmentation of dynamic renal MR images", Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754617 (26 February 2010); https://doi.org/10.1117/12.853474

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