Abstract #3517

Segmenting Brain Tumor Lesion from 3D FLAIR MR Images using Support Vector Machine approach

Virendra Kumar Yadav¹, Neha Vats¹, Manish Awasthi¹, Dinil Sasi¹, Mamta Gupta², Rakesh Kumar Gupta², Sumeet Agarwal³, and Anup Singh^1,4

¹Center for Biomedical Engineering, Indian Institute of Technology, Delhi, India, ²Fortis Memorial Research Institute, Gurugram, India, ³Electrical Engineering, Indian Institute of Technology, Delhi, India, ⁴Biomedical Engineering, AIIMS, New Delhi, India

Segmentation of brain tumor lesion is important for diagnosis and treatment planning. Tumor tissue and edema usually appears hyperintense on fluid-attenuated-inversion-recovery (FLAIR) MR images. FLAIR images are widely used for brain tumor localization and segmentation purpose. In this study, a Support-Vector-Machine (SVM) model was developed for segmentation of FLAIR hyper-intense region semi-automatically using BraTS 2018 dataset. The proposed approach require a minimal user involvement in selecting one region around tumor in the central slice. It was observed that proposed SVM approach segmentation results shows better dice coefficient in comparison to what reported in literature.

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