Automatic brain tumor detection in Magnetic Resonance Images

  title={Automatic brain tumor detection in Magnetic Resonance Images},
  author={Sahar Ghanavati and Junning Li and Ting Liu and Paul S. Babyn and Wendy Doda and George A. Lampropoulos},
  journal={2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)},
Automatic detection of brain tumor is a difficult task due to variations in type, size, location and shape of tumors. In this paper, a multi-modality framework for automatic tumor detection is presented, fusing different Magnetic Resonance Imaging modalities including T1-weighted, T2-weighted, and T1 with gadolinium contrast agent. The intensity, shape deformation, symmetry, and texture features were extracted from each image. The AdaBoost classifier was used to select the most discriminative… CONTINUE READING

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Key Quantitative Results

  • Preliminary results on simulated and patient MRI show 100% successful tumor detection with average accuracy of 90.11%.


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