Όνομα Περιοδικού:e-Journal of Science & Technology e-Περιοδικό Επιστήμης & Τεχνολογίας
Three dimensional texture analysis of volumetric brain MR images have been identified as an
important indicator for discriminating among different brain pathologies. The aim of the present study
was to evaluate the efficiency of three dimensional textural features using a pattern recognition system
in the task of discriminating primary from metastatic brain tissues on T1 post-contrast MRI series. The
dataset consisted of sixty seven brain MRI series obtained from patients with verified and untreated
intracranial tumors. The pattern recognition system was designed employing a probabilistic neural
network classifier, specially modified in order to integrate the non-linear least squares feature
transformation logic in its discriminant function. The latter, in conjunction with using three
dimensional textural features, enabled boosting up the performance of the system in discriminating
primary from metastatic with accuracy of 95.52%. The proposed system might be used as an assisting
tool for brain tumor characterization on volumetric MRI series.