Zusammenfassung
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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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Xu, Y., Schebesch, F., Ravikumar, N., Maier, A. (2019). Detection of Unseen Low-Contrast Signals Using Classic and Novel Model Observers. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_47
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DOI: https://doi.org/10.1007/978-3-658-25326-4_47
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