In this section
@inproceedings{Soltaninejad:2014,
author = {Soltaninejad, M and Ye, X and Yang, G and Allinson, N and Lambrou, T},
publisher = {British Machine Vision Association},
title = {Brain tumour grading in different MRI protocols using SVM on statistical features},
year = {2014}
}
TY - CPAPER
AB - 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.
AU - Soltaninejad,M
AU - Ye,X
AU - Yang,G
AU - Allinson,N
AU - Lambrou,T
PB - British Machine Vision Association
PY - 2014///
TI - Brain tumour grading in different MRI protocols using SVM on statistical features
ER -
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