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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