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Proceedings Paper

Multiclass feature selection for improved pediatric brain tumor segmentation
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Paper Abstract

In our previous work, we showed that fractal-based texture features are effective in detection, segmentation and classification of posterior-fossa (PF) pediatric brain tumor in multimodality MRI. We exploited an information theoretic approach such as Kullback-Leibler Divergence (KLD) for feature selection and ranking different texture features. We further incorporated the feature selection technique with segmentation method such as Expectation Maximization (EM) for segmentation of tumor T and non tumor (NT) tissues. In this work, we extend the two class KLD technique to multiclass for effectively selecting the best features for brain tumor (T), cyst (C) and non tumor (NT). We further obtain segmentation robustness for each tissue types by computing Bay's posterior probabilities and corresponding number of pixels for each tissue segments in MRI patient images. We evaluate improved tumor segmentation robustness using different similarity metric for 5 patients in T1, T2 and FLAIR modalities.

Paper Details

Date Published: 23 February 2012
PDF: 10 pages
Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83153J (23 February 2012); doi: 10.1117/12.911018
Show Author Affiliations
Shaheen Ahmed, The Univ. of Memphis (United States)
Khan M. Iftekharuddin, The Univ. of Memphis (United States)
Old Dominion Univ. (United States)

Published in SPIE Proceedings Vol. 8315:
Medical Imaging 2012: Computer-Aided Diagnosis
Bram van Ginneken; Carol L. Novak, Editor(s)

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