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.
Georgiadis
Pantelis
P
Medical Image Processing and Analysis Group, Laboratory of Medical Physics, School of Medicine, University of Patras, Rio GR-26503, Greece. pgeorgiadis@med.upatras.gr
Cavouras
Dionisis
D
Kalatzis
Ioannis
I
Daskalakis
Antonis
A
Kagadis
George C
GC
Sifaki
Koralia
K
Malamas
Menelaos
M
Nikiforidis
George
G
Solomou
Ekaterini
E
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