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Proceedings Paper

Multi-fractal texture features for brain tumor and edema segmentation
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Paper 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.

Paper Details

Date Published: 18 March 2014
PDF: 10 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903503 (18 March 2014); doi: 10.1117/12.2044264
Show Author Affiliations
S. Reza, Old Dominion Univ. (United States)
K. M. Iftekharuddin, Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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