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Published in:
2014 | OriginalPaper | Chapter
Magnetic Resonance Texture Analysis: Optimal Feature Selection in Classifying Child Brain Tumors
Authors : Suchada Tantisatirapong, Nigel P. Davies, Daniel Rodriguez, Laurence Abernethy, Dorothee P. Auer, C. A. Clark, Richard Grundy, Tim Jaspan, Darren Hargrave, Lesley MacPherson, Martin O. Leach, Geoffrey S. Payne, Barry L. Pizer, Andrew C. Peet, Theodoros N. Arvanitis
Published in: XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013
Publisher: Springer International Publishing
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Textural feature based classification has shown that magnetic resonance images can characterize histological brain tumor types. Feature selection is an important process to acquire a robust textural feature subset and enhance classification rate. This work investigates two different feature selection techniques; principal component analysis (PCA), and the combination of max-relevance and min-redundancy (mRMR) and feedforward selection. We validated these techniques based on a multi-center dataset of pediatric brain tumor types; medulloblastoma, pilocytic astrocytoma and ependymoma, and investigated the accuracy of tumor classification, based on textural features of diffusion and conventional MR images.