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. © Springer International ational Publishing Switzerland 2014.
CITATION STYLE
Tantisatirapong, S., Davies, N. P., Rodriguez, D., Abernethy, L., Auer, D. P., Clark, C. A., … Arvanitis, T. N. (2014). Magnetic resonance texture analysis: Optimal feature selection in classifying child brain tumors. In IFMBE Proceedings (Vol. 41, pp. 309–312). Springer Verlag. https://doi.org/10.1007/978-3-319-00846-2_77
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