Non-linear least squares features transformation for improving the performance of probabilistic neural networks in classifying human brain tumors on MRI

  • Authors:

  • Pantelis Georgiadis;Dionisis Cavouras;Ioannis Kalatzis;Antonis Daskalakis;George Kagadis;Koralia Sifaki;Menelaos Malamas;George Nikiforidis;Ekaterini Solomou

  • Affiliations:

  • Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio, Greece;Medical Signal and Image Processing Lab, Department of Medical Instruments Technology, Technological Educational Institute of Athens, Athens, Greece;Medical Signal and Image Processing Lab, Department of Medical Instruments Technology, Technological Educational Institute of Athens, Athens, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio, Greece;General Hellenic Airforce Hospital, MRI Unit, Katehaki, Athens, Greece;General Hellenic Airforce Hospital, MRI Unit, Katehaki, Athens, Greece;Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio, Greece;Department of Radiology, School of Medicine, University of Patras, Rio, Greece

  • Venue:
  • ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
  • Year:
  • 2007

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Abstract

The aim of the present study was to design, implement, and evaluate a software system for discriminating between metastases, meningiomas, and gliomas on MRI. The proposed classifier is a modified probabilistic neural network (PNN), incorporating a second degree least squares features transformation (LSFT) into the PNN classifier. Thirty-six textural features were extracted from each one of 75 T1-weighted post-contrast MR images (24 metastases, 21 meningiomas, and 30 gliomas). Classification performance was evaluated employing the leave-one-out method and for all possible textural feature combinations. LSFT enhanced the performance of the PNN, achieving 93.33%in discriminating between the three major types of human brain tumors, against 89.33% scored by the PNN alone. Best feature combination for achieving highest discrimination power included the mean value and entropy, which reflect specific properties of texture, i.e. signal strength and inhomogeneity. LSFT improved PNN performance, increased class separability, and resulted in dimensionality reduction.