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Abstract #2493

A new approach for automatic image quality assessment

Thomas Kstner 1,2 , Parnia Bahar 2 , Christian Wrslin 1 , Sergios Gatidis 1 , Petros Martirosian 3 , Nina Schwenzer 1 , Holger Schmidt 1 , and Bin Yang 2

1 Department of Radiology, University Hospital of Tbingen, Tbingen, Baden-Wrttemberg, Germany, 2 Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Baden-Wrttemberg, Germany, 3 Diagnostic and Interventional Radiology, University Hospital of Tbingen, Tbingen, Baden-Wrttemberg, Germany

A reliable and meaningful image quality assessment can be very demanding, especially when there is no reference or gold-standard available. Evaluation mainly depends on human observers, but due to the huge amount of acquired data, this task can be very time-consuming and costly. Hence an automatic evaluation is desired. We therefore propose a robust, accurate and flexible automatic evaluation system which is based on a machine-learning approach to evaluate certain diagnostic questions dependent on the chosen application and trained input data. Our framework achieves a test accuracy of 91.2% and hence can be used for automatic quality classification.

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