title: Multi-fractal Detrended Texture Feature for Brain Tumor Classification.
abstract: We propose a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) [1] in structural magnetic resonance (MR) images. This preliminary work investigates the efficacy of the MFDFA features along with our novel texture feature known as multi-fractional Brownian motion (mBm) [2]in classifying (grading) brain tumors as High Grade (HG) and Low Grade (LG). Based on prior performance, Random Forest (RF) [3] is employed for tumor grading using two different datasets such as BRATS-2013 [4] and BRATS-2014 [5]. Quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. On an average 90% precision and 85% recall from the inter-dataset cross-validation confirm the efficacy of the proposed method.
authors.affiliation: Old Dominion University, Norfolk, Virginia -23529.
authors.forename: Syed M S
authors.initials: SM
authors.lastname: Reza
authors.affiliation: Old Dominion University, Norfolk, Virginia -23529.
authors.forename: Randall
authors.initials: R
authors.lastname: Mays
authors.affiliation: Old Dominion University, Norfolk, Virginia -23529.
authors.forename: Khan M
authors.initials: KM
authors.lastname: Iftekharuddin
keywords: MFDFA; MR; brain tumor; classification; mBm; random forest; texture; tumor grade