期刊
MEDICAL IMAGING 2015: COMPUTER-AIDED DIAGNOSIS
卷 9414, 期 -, 页码 -出版社
SPIE-INT SOC OPTICAL ENGINEERING
DOI: 10.1117/12.2083596
关键词
classification; brain tumor; MFDFA; mBm; random forest; MR; texture; tumor grade
资金
- NCI NIH HHS [R15 CA115464] Funding Source: Medline
- NIBIB NIH HHS [R01 EB020683] Funding Source: Medline
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.
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