Kuhnert N, Maass N, Barth K, Maier A (2017)
Publication Type: Conference contribution
Publication year: 2017
Publisher: Kluwer Academic Publishers
Pages Range: 92-97
Conference Proceedings Title: Informatik aktuell
Event location: Berlin, DEU
ISBN: 9783662494646
DOI: 10.1007/978-3-662-49465-3_18
Metal artifact reduction (MAR) is crucial for the diagnostic value, as metal artifacts tremendously impair the image quality of a CT scan. Existing techniques are time-consuming. Most MAR methods contain a metal object segmentation step and the resulting image quality highly depends on the validity of the segmentation. However, segmenting the metal parts correctly still poses a non-trivial problem. 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 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). The resulting CT images yielded higher image quality compared with the standard threshold-based FSMAR.
APA:
Kuhnert, N., Maass, N., Barth, K., & Maier, A. (2017). Reduction of metal artifacts using a new segmentation approach: Extension of graph cuts for a more precise segmentation used in metal artifact reduction. In Thomas M. Deserno, Heinz Handels, Thomas Tolxdorff, Hans-Peter Meinzer (Eds.), Informatik aktuell (pp. 92-97). Berlin, DEU: Kluwer Academic Publishers.
MLA:
Kuhnert, Nadine, et al. "Reduction of metal artifacts using a new segmentation approach: Extension of graph cuts for a more precise segmentation used in metal artifact reduction." Proceedings of the Workshops on Image processing for the medicine, 2016, Berlin, DEU Ed. Thomas M. Deserno, Heinz Handels, Thomas Tolxdorff, Hans-Peter Meinzer, Kluwer Academic Publishers, 2017. 92-97.
BibTeX: Download