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

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.

authors

Georgiadis P,Cavouras D,Kalatzis I,Daskalakis A,Kagadis GC,Sifaki K,Malamas M,Nikiforidis G,Solomou E

doi

10.1016/j.cmpb.2007.10.007

subject

Has Abstract

pub_date

2008-01-01 00:00:00

pages

24-32

issue

1

eissn

0169-2607

issn

1872-7565

pii

S0169-2607(07)00240-4

journal_volume

89

pub_type

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