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

Xu Y, Schebesch F, Ravikumar N, Maier A (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Publisher: Springer Berlin Heidelberg

Pages Range: 212-217

Conference Proceedings Title: Informatik aktuell

Event location: Lübeck

ISBN: 9783658253257

DOI: 10.1007/978-3-658-25326-4_47

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.

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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.

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