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3 March 2009Efficacy of texture, shape, and intensity features for robust posterior-fossa tumor segmentation in MRI
Our previous works suggest that fractal-based texture features are very useful for detection, segmentation and
classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. In this work, we investigate and
compare efficacy of our texture features such as fractal and multifractional Brownian motion (mBm), and intensity
along with another useful level-set based shape feature in PF tumor segmentation. We study feature selection and
ranking using Kullback -Leibler Divergence (KLD) and subsequent tumor segmentation; all in an integrated
Expectation Maximization (EM) framework. We study the efficacy of all four features in both multimodality as well
as disparate MRI modalities such as T1, T2 and FLAIR. Both KLD feature plots and information theoretic entropy
measure suggest that mBm feature offers the maximum separation between tumor and non-tumor tissues in T1 and
FLAIR MRI modalities. The same metrics show that intensity feature offers the maximum separation between tumor
and non-tumor tissue in T2 MRI modality. The efficacies of these features are further validated in segmenting PF
tumor using both single modality and multimodality MRI for six pediatric patients with over 520 real MR images.
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S. Ahmed, K. M. Iftekharuddin, R. J. Ogg, F. H. Laningham, "Efficacy of texture, shape, and intensity features for robust posterior-fossa tumor segmentation in MRI," Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726020 (3 March 2009); https://doi.org/10.1117/12.813875