Three-dimensional texture analysis of MRI brain datasets
VA Kovalev, F Kruggel, HJ Gertz… - IEEE transactions on …, 2001 - ieeexplore.ieee.org
VA Kovalev, F Kruggel, HJ Gertz, DY von Cramon
IEEE transactions on medical imaging, 2001•ieeexplore.ieee.orgA method is proposed for three-dimensional (3-D) texture analysis of magnetic resonance
imaging brain datasets. It is based on extended, multisort co-occurrence matrices that
employ intensity, gradient and anisotropy image features in a uniform way. Basic properties
of matrices as well as their sensitivity and dependence on spatial image scaling are
evaluated. The ability of the suggested 3-D texture descriptors is demonstrated on nontrivial
classification tasks for pathologic findings in brain datasets.
imaging brain datasets. It is based on extended, multisort co-occurrence matrices that
employ intensity, gradient and anisotropy image features in a uniform way. Basic properties
of matrices as well as their sensitivity and dependence on spatial image scaling are
evaluated. The ability of the suggested 3-D texture descriptors is demonstrated on nontrivial
classification tasks for pathologic findings in brain datasets.
A method is proposed for three-dimensional (3-D) texture analysis of magnetic resonance imaging brain datasets. It is based on extended, multisort co-occurrence matrices that employ intensity, gradient and anisotropy image features in a uniform way. Basic properties of matrices as well as their sensitivity and dependence on spatial image scaling are evaluated. The ability of the suggested 3-D texture descriptors is demonstrated on nontrivial classification tasks for pathologic findings in brain datasets.
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