This paper presents a novel method for Glioblastoma (GBM) feature extraction based on Gaussian mixture model (GMM) features\nusing MRI. We addressed the task of the new features to identify GBM using T1 and T2 weighted images (T1-WI, T2-WI) and\nFluid-Attenuated Inversion Recovery (FLAIR) MR images. A pathologic area was detected using multithresholding segmentation\nwith morphological operations of MR images.Multi classifier techniques were considered to evaluate the performance of the feature\nbased scheme in terms of its capability to discriminate GBM and normal tissue.GMM features demonstrated the best performance\nby the comparative study using principal component analysis (PCA) and wavelet based features. For the T1-WI, the accuracy\nperformance was 97.05% (AUC = 92.73%) with 0.00% missed detection and 2.95% false alarm. In the T2-WI, the same accuracy\n(97.05%, AUC = 91.70%) value was achieved with 2.95% missed detection and 0.00% false alarm. In FLAIR mode the accuracy\ndecreased to 94.11% (AUC = 95.85%) with 0.00% missed detection and 5.89% false alarm. These experimental results are promising\nto enhance the characteristics of heterogeneity and hence early treatment of GBM.
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