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18 March 2014 Multi-fractal texture features for brain tumor and edema segmentation
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Abstract
In this work, we propose a fully automatic brain tumor and edema segmentation technique in brain magnetic resonance (MR) images. Different brain tissues are characterized using the novel texture features such as piece-wise triangular prism surface area (PTPSA), multi-fractional Brownian motion (mBm) and Gabor-like textons, along with regular intensity and intensity difference features. Classical Random Forest (RF) classifier is used to formulate the segmentation task as classification of these features in multi-modal MRIs. The segmentation performance is compared with other state-of-art works using a publicly available dataset known as Brain Tumor Segmentation (BRATS) 2012 [1]. Quantitative evaluation is done using the online evaluation tool from Kitware/MIDAS website [2]. The results show that our segmentation performance is more consistent and, on the average, outperforms other state-of-the art works in both training and challenge cases in the BRATS competition.
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S. Reza and K. M. Iftekharuddin "Multi-fractal texture features for brain tumor and edema segmentation", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903503 (18 March 2014); https://doi.org/10.1117/12.2044264
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