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Volumetric analysis of MR images for glioma classification and their effect on brain tissues


This work aims to identify non-invasive quantitative parameters from three-dimensional brain magnetic resonance images in order: (1) to classify brain tumor (glioma) as low grade (LG) or high grade (HG) and (2) to analyze effect of tumor on brain gray matter (GM) and white matter (WM). In proposed model features were extracted from segmented tumor region based on its volume and shape for distinguishing the tumor grade. Statistical analysis revealed good correlation between segmented tumor volume and tumor grade. Various morphological parameters extracted from segmented tumor region were also significantly different for LG and HG cases (\(p<0.05\)). Also, for brain tumor patients a considerable variation in normalized GM (%GM) volume was obtained compared to normalized WM (%WM) volume for LG and HG cases. We also found that, relative to controls, there was higher effect on %GM and %WM volumes for HG glioma patients as compared to LG glioma patients. The experimental results show that proposed feature set achieves LG/HG classification with high accuracy using support vector machines classifier.

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We thank Government of India for providing the grant for the research work under JRF-UGC-NET (Grant No. 4037, June 2013) scholarship scheme.

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Correspondence to Manu Gupta.

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Gupta, M., Rajagopalan, V., Pioro, E.P. et al. Volumetric analysis of MR images for glioma classification and their effect on brain tissues. SIViP 11, 1337–1345 (2017).

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  • Brain tumor
  • Glioma
  • Magnetic resonance imaging
  • Gray matter
  • White matter
  • Feature extraction
  • Classification