论文信息 - Brain Tumour Grading in Different MRI Protocols using SVM on Statistical Features

Brain Tumour Grading in Different MRI Protocols using SVM on Statistical Features

In this paper a feasibility study of brain MRI dataset classification, using ROIs which have been segmented either manually or through a superpixel based method in conjunction with statistical pattern recognition methods is presented. In our study, 471 extracted ROIs from 21 Brain MRI datasets are used, in order to establish which features distinguish better between three grading classes. Thirty-eight statistical measurements were collected from the ROIs. We found by using the Leave-One-Out method that the combination of the features from the 1st and 2nd order statistics, achieved high classification accuracy in pair-wise grading comparisons.

[1] V. M. Misra,et al. Classification of Brain Cancer using Artificial Neural Network , 2010, 2010 2nd International Conference on Electronic Computer Technology.

[2] Ιωάννης Καλατζής,et al. Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features , 2015 .

[3] Christos Davatzikos,et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme , 2009, Magnetic resonance in medicine.