Abstract #4869

Feature Reduction and Selection: a Study on their Importance in the Context of Radiomics

Annika Liebgott^1,2, Janik Steyer-Ege², Tobias Hepp¹, Thomas Küstner^1,2,3, Konstantin Nikolaou¹, Bin Yang², and Sergios Gatidis¹

¹Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tübingen, Germany, ²Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, ³School of Biomedical Engineering and Imaging Sciences, King's College London/St Thomas' Hospital, London, United Kingdom

Using large amounts of image features in the context of Radiomics to perform complex image analysis tasks yields promising results for clinical applications. While it is easy to extract a large amount of features from medical images, it is complex to select the right features for a specific scientific problem. This study aims to show, how important it is to pay attention to choosing the right technique to select the most suitable features by means of feature reduction or selection on the example of two Radiomics-related MR image classification tasks.

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