Zusammenfassung
In this work, the application of traditional machine learning techniques, in the form of regression models based on conventional, “hand-crafted” features, to streak reduction in limited angle tomography is investigated. Specifically, linear regression (LR), multi-layer perceptron (MLP), and reduced-error pruning tree (REPTree) are investigated. When choosing the mean-variation-median (MVM), Laplacian, and Hessian features, REPTree learns streak artifacts best and reaches the smallest root-mean-square error (RMSE) of 29HU for the Shepp-Logan phantom. Further experiments demonstrate that the MVM and Hessian features complement each other, whereas the Laplacian feature is redundant in the presence of MVM. Preliminary experiments on clinical data suggests that further investigation of clinical applications using REPTree may be worthwhile.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
Literatur
Huang Y, Huang X, Taubmann O, et al. Restoration of missing data in limited angle tomography based on Helgason-Ludwig consistency conditions. Biomed Phys Eng Express. 2017;3(3).
Chen Z, Jin X, Li L, et al. A limited-angle CT reconstruction method based on anisotropic TV minimization. Biomed Phys Eng Express. 2013;58(7).
Huang Y, Taubmann O, Huang X, et al. A new scale space total variation algorithm for limited angle tomography. Proc CT-Meeting. 2016;3(3):149–152.
Würfl T, Ghesu F, Christlein V, et al. Deep learning computed tomography. Proc CT-Meeting. 2016;3(3):432–440.
Hammernik K, Würfl T, Pock T, et al. A deep learning architecture for limitedangle computed tomography reconstruction. Proc CT-Meeting. 2017;3(3):92–97.
Gu J, Ye J. Multi-scale wavelet domain residual learning for limited-angle CT reconstruction. Proc CT-Meeting. 2017;3(3):443–447.
Loh W. Classification and regression trees. Proc CT-Meeting. 2011;1(1):14–23.
Quinlan J. Simplifying decision trees. Proc CT-Meeting. 1987;27(3):221–234.
Frank E, Hall M, Witten I. The WEKA workbench: online appendix for data mining: practical machine learning tools and techniques.. vol. 27. Int J Man Mach Stud.; 2016.
Maier A, Berger M, Fischer P, et al. CONRAD: a software framework for conebeam imaging in radiology. Med Phys. 2013;40(11):111914.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer-Verlag GmbH Deutschland
About this paper
Cite this paper
Huang, Y., Lu, Y., Taubmann, O., Lauritsch, G., Maier, A. (2018). Traditional Machine Learning Techniques for Streak Artifact Reduction in Limited Angle Tomography. In: Maier, A., Deserno, T., Handels, H., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2018. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56537-7_62
Download citation
DOI: https://doi.org/10.1007/978-3-662-56537-7_62
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-56536-0
Online ISBN: 978-3-662-56537-7
eBook Packages: Computer Science and Engineering (German Language)