Traditional machine learning for limited angle tomography.

Journal: International journal of computer assisted radiology and surgery

Published Date: Aug 22, 2018

Abstract

PURPOSE: The application of traditional machine learning techniques, in the form of regression models based on conventional, "hand-crafted" features, to artifact reduction in limited angle tomography is investigated.

Authors

Yixing Huang

Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany. yixing.yh.huang@fau.de.
Yanye Lu

Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany. yanye.lu@fau.de.
Oliver Taubmann

Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058, Erlangen, Germany.
Guenter Lauritsch

Siemens Healthcare GmbH, Siemensstr. 1, 91301, Forchheim, Germany.
Andreas Maier

Pattern Recognition Lab, University Erlangen-Nürnberg, Erlangen, Germany.

Keywords

Algorithms Artifacts Humans Image Processing, Computer-Assisted Machine Learning Phantoms, Imaging Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (30136109)

Traditional machine learning for limited angle tomography.

Abstract

Authors

Keywords

External Resources

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