Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features.
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.
Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio GR-26503, Greece. pgeorgiadis@med.upatras.gr
Pantelis
P
Georgiadis
Dionisis
D
Cavouras
Ioannis
I
Kalatzis
Antonis
A
Daskalakis
George C
GC
Kagadis
Koralia
K
Sifaki
Menelaos
M
Malamas
George
G
Nikiforidis
Ekaterini
E
Solomou
D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology / Q000556:secondary; D003663:Decision Trees; D003937:Diagnosis, Differential; D005910:Glioma / Q000175:diagnosis / Q000473:pathology / Q000556:secondary; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008579:Meningioma / Q000175:diagnosis / Q000473:pathology / Q000556:secondary; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012984:Software