Classification of body regions based on MRI log files

Kuhnert N, Lindenmayr O, Maier A (2018)

Publication Type: Book chapter / Article in edited volumes

Publication year: 2018

Publisher: Springer Verlag

Edited Volumes: Proceedings of the 10th International Conference on Computer Recognition Systems, CORES 2017

Series: Advances in Intelligent Systems and Computing

Book Volume: 578

Pages Range: 102-109

DOI: 10.1007/978-3-319-59162-9_11

Abstract

Every Siemens Magnetic Resonance Imaging (MRI) system consistently writes events into log files while the system is running. The log files and their contents are constantly refined by software developers. This results in different information contents depending on the software version. One information that is missing in some log files is the examined body region. As the body region is crucial for usage analysis, we used pattern recognition methods to estimate the examined body region for software versions not logging it automatically. We learned the examined body region from a set of used MRI acquisition parameters such as grid and voxel size and could classify body region information with a classification rate up to 94.7%. We compared Bayesian Network augmented Naïve Bayes, Decision Trees, and Neural Networks, and found Neural Networks resulting in the best classification rate.

Authors with CRIS profile

Nadine Rücker Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens Healthineers DE Germany (DE)

How to cite

APA:

Kuhnert, N., Lindenmayr, O., & Maier, A. (2018). Classification of body regions based on MRI log files. In Marek Kurzynski, Michal Wozniak, Robert Burduk (Eds.), Proceedings of the 10th International Conference on Computer Recognition Systems, CORES 2017. (pp. 102-109). Springer Verlag.

MLA:

Kuhnert, Nadine, Oliver Lindenmayr, and Andreas Maier. "Classification of body regions based on MRI log files." Proceedings of the 10th International Conference on Computer Recognition Systems, CORES 2017. Ed. Marek Kurzynski, Michal Wozniak, Robert Burduk, Springer Verlag, 2018. 102-109.

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