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Proceedings Paper

Efficacy of texture, shape, and intensity features for robust posterior-fossa tumor segmentation in MRI
Author(s): S. Ahmed; K. M. Iftekharuddin; R. J. Ogg; F. H. Laningham
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Paper Abstract

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.

Paper Details

Date Published: 3 March 2009
PDF: 12 pages
Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 726020 (3 March 2009); doi: 10.1117/12.813875
Show Author Affiliations
S. Ahmed, The Univ. of Memphis (United States)
K. M. Iftekharuddin, The Univ. of Memphis (United States)
R. J. Ogg, St. Jude Children's Research Hospital (United States)
F. H. Laningham, St. Jude Children's Research Hospital (United States)

Published in SPIE Proceedings Vol. 7260:
Medical Imaging 2009: Computer-Aided Diagnosis
Nico Karssemeijer; Maryellen L. Giger, Editor(s)

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