Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features

Authors:
Pantelis Georgiadis;Dionisis Cavouras;Ioannis Kalatzis;Antonis Daskalakis;George C. Kagadis;Koralia Sifaki;Menelaos Malamas;George Nikiforidis;Ekaterini Solomou
Affiliations:
Medical Image Processing and Analysis (MIPA) Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio GR-26503, Greece;Medical Image and Signal Processing Laboratory, Department of Medical Instrumentation Technology, Technological Education Institution of Athens, Ag. Spyridonos Street, Aigaleo GR-12210, Athens, Gr ...;Medical Image and Signal Processing Laboratory, Department of Medical Instrumentation Technology, Technological Education Institution of Athens, Ag. Spyridonos Street, Aigaleo GR-12210, Athens, Gr ...;Medical Image and Signal Processing Laboratory, Department of Medical Instrumentation Technology, Technological Education Institution of Athens, Ag. Spyridonos Street, Aigaleo GR-12210, Athens, Gr ...;Medical Image and Signal Processing Laboratory, Department of Medical Instrumentation Technology, Technological Education Institution of Athens, Ag. Spyridonos Street, Aigaleo GR-12210, Athens, Gr ...;251 General Hellenic Airforce Hospital, MRI Unit, Katehaki, Athens, Greece;251 General Hellenic Airforce Hospital, MRI Unit, Katehaki, Athens, Greece;Medical Image Processing and Analysis (MIPA) Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio GR-26503, Greece;Department of Radiology, School of Medicine, University of Patras, Rio GR-26503, Greece
Venue:
Computer Methods and Programs in Biomedicine
Year:
2008

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Abstract

The aim of the present study was to design, implement and evaluate a software system for discriminating between metastatic and primary brain tumors (gliomas and meningiomas) on MRI, employing textural features from routinely taken T1 post-contrast images. The proposed classifier is a modified probabilistic neural network (PNN), incorporating a non-linear least squares features transformation (LSFT) into the PNN classifier. Thirty-six textural features were extracted from each one of 67 T1-weighted post-contrast MR images (21 metastases, 19 meningiomas and 27 gliomas). LSFT enhanced the performance of the PNN, achieving classification accuracies of 95.24% for discriminating between metastatic and primary tumors and 93.48% for distinguishing gliomas from meningiomas. To improve the generalization of the proposed classification system, the external cross-validation method was also used, resulting in 71.43% and 81.25% accuracies in distinguishing metastatic from primary tumors and gliomas from meningiomas, respectively. LSFT improved PNN performance, increased class separability and resulted in dimensionality reduction.