Abstract
Automatic analysis of MR images requires the correct identification of the various tissues within them. Cortical bone is the most challenging tissue to identify in MR. We present an algorithm to automatically predict the cortical bone locations from whole-body MR Dixon images. Our algorithm combines local information from MR with global information borrowed from exemplar patients with co-registered MR and CT images. The local information is calculated using a classifier trained to discriminate bone from soft tissue using new multi-image template features. The global information is incorporated by retrieving annotated bone maps of the exemplars using a new, non-rigid registration algorithm. We combine the local and global information by an iterative filtering precedure.
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References
Chefd’hotel, C., Hermosillo, G., Faugeras, O.: Flows of diffeomorphisms for multimodal image registration. In: International Symposium on Biomedical Imaging. IEEE (2002)
Hermosillo, G., Chefd’hotel, C., Faugeras, O.: Variational methods for multimodal image matching. International Journal of Computer Vision 50(3), 329–343 (2002)
Hofmann, M., Steinke, F., Scheel, V., Charpiat, G., Farquhar, J., Aschoff, P., Brady, M., Scholkopf, B., Pichler, B.J.: MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration. Journal of Nuclear Medicine 49, 1875–1883 (2008)
Krishnapuram, B., Carin, L., Figueiredo, M.A.T., Hartemink, A.J.: Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 957–968 (2005)
Merkle, E.M., Nelson, R.C.: Dual Gradient-Echo In-Phase and Opposed-Phase Hepatic MR Imaging: A Useful Tool for Evaluating More Than Fatty Infiltration or Fatty Sparing. Radiographics 26(5), 1409–1418 (2006)
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© 2012 Springer-Verlag Berlin Heidelberg
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Hermosillo, G., Raykar, V.C., Zhou, X. (2012). Learning to Locate Cortical Bone in MRI. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_21
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DOI: https://doi.org/10.1007/978-3-642-35428-1_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35427-4
Online ISBN: 978-3-642-35428-1
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