Multi-fractal detrended texture feature for brain tumor classification

Syed M. S. Reza; Randall Mays; Khan M. Iftekharuddin

doi:10.1117/12.2083596

20 March 2015 Multi-fractal detrended texture feature for brain tumor classification

Syed M. S. Reza, Randall Mays, Khan M. Iftekharuddin

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 941410 (2015) https://doi.org/10.1117/12.2083596
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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 multifractional 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.

Citation Download Citation

Syed M. S. Reza, Randall Mays, and Khan M. Iftekharuddin "Multi-fractal detrended texture feature for brain tumor classification", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 941410 (20 March 2015); https://doi.org/10.1117/12.2083596

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