Detection of Unseen Low-Contrast Signals Using Classic and Novel Model Observers

Conference contribution


Publication Details

Author(s): Xu Y, Schebesch F, Ravikumar N, Maier A
Editor(s): Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff
Publisher: Springer Berlin Heidelberg
Publication year: 2019
Conference Proceedings Title: Informatik aktuell
Pages range: 212-217
ISBN: 9783658253257
ISSN: 1431-472X


Abstract

Automatic task-based image quality assessment has been of importance in various clinical and research applications. In this paper, we propose a neural network model observer, a novel concept which has recently been investigated. It is trained and tested on simulated images with different contrast levels, with the aim of trying to distinguish images based on their quality/contrast. Our model shows promising properties that its output is sensitive to image contrast, and generalizes well to unseen low-contrast signals. We also compare the results of the proposed approach with those of a channelized hotelling observer (CHO), on the same simulated dataset.


FAU Authors / FAU Editors

Maier, Andreas Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Ravikumar, Nishant
Lehrstuhl für Informatik 5 (Mustererkennung)
Schebesch, Frank
Lehrstuhl für Informatik 5 (Mustererkennung)
Xu, Yang
Lehrstuhl für Informatik 12 (Hardware-Software-Co-Design)


How to cite

APA:
Xu, Y., Schebesch, F., Ravikumar, N., & Maier, A. (2019). Detection of Unseen Low-Contrast Signals Using Classic and Novel Model Observers. In Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 212-217). Lübeck, DE: Springer Berlin Heidelberg.

MLA:
Xu, Yang, et al. "Detection of Unseen Low-Contrast Signals Using Classic and Novel Model Observers." Proceedings of the Workshop on Bildverarbeitung fur die Medizin, 2019, Lübeck Ed. Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff, Springer Berlin Heidelberg, 2019. 212-217.

BibTeX: 

Last updated on 2019-20-05 at 09:53