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Abstract #1498

GLCM texture analysis of knee cartilage T2 maps: machine learning based selection of important features

Veronika Janacova1, Pavol Szomolanyi1, Dominik Vilimek1,2, Siegfried Trattnig1,3,4, and Vladimir Juras1
1High Field MR Centre, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, 2Department of Cybernetics and Biomedical Engineering, VSBā€“Technical University of Ostrava, Ostrava, Czech Republic, 3CD laboratory for Clinical Molecular MR imaging (MOLIMA), Vienna, Austria, 4Institute for Clinical Molecular MRI in the Musculoskeletal System, Karl Landsteiner Society, Vienna, Austria


Texture analysis of quantitative T2 maps in combination with machine learning was explored as a tool for classification and prediction of various conditions in musculoskeletal (MSK) research. We explored random forest classification algorithm as a tool for identification of important texture features for the classification of MACT grafts and native cartilage twelve months after surgery. Our model performed with high accuracy (84.6%) and identified features with highest importance were: cluster prominence, sum average, autocorrelation and correlation.

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