Reduction of Metal Artifacts using a New Segmentation Approach

Kuhnert N, Barth K, Maass N, Maier A (2016)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2016

Abstract

Metal artifact reduction (MAR) is crucial for the diagnostic value, as metal artifacts tremendously impair the image quality of a CT scan. Wang et al. [1] presented an iterative MAR approach, whereas Kachelriess et al. [2] proposed a filtering method to reduce metal artifacts. However, both techniques are very time-consuming. Most MAR methods contain a metal object segmentation step and the resulting image quality highly depends on the validity of the segmentation [3]. However, segmenting the metal parts correctly poses a non-trivial problem due to the metal artifacts. We present a novel approach of an automatic, object independent segmentation which starts with the state-of-the-art segmentation. This is improved by applying graph cut [4] onto every projection. We extend the graph cut idea by more information and apply knowledge about the distance, a classification probability and a bias to the edges as well as a similarity measure of pixels to their direct neighbors. By additionally considering global consistency, we receive a more precise segmentation result. For the evaluation, our new segmentation approach was combined with the frequency split MAR (FSMAR) [5]. The resulting CT images yielded higher image quality compared with the standard threshold-based FSMAR.

Authors with CRIS profile

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

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Kuhnert, N., Barth, K., Maass, N., & Maier, A. (2016). Reduction of Metal Artifacts using a New Segmentation Approach. In Proceedings of the Bildverarbeitung für die Medizin.

MLA:

Kuhnert, Nadine, et al. "Reduction of Metal Artifacts using a New Segmentation Approach." Proceedings of the Bildverarbeitung für die Medizin 2016.

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