| Popis: |
In neuroimaging, magnetic resonance images can be used to locate and obtain various parameters in order to find a wide range of pathologies, improving diagnosis and hence early treatment. Since images of the brain are volumetric, they are treated volumetrically in voxels, rather than planarly in pixels. We present an alternative implementation of local neighborhood-based texture parameters that have been recently shown to improve the detection of differences in the brain between healthy patients and those with Alzheimer's disease using diffusion tensor imaging [1]. We implemented the method (1) in Python using the Anaconda environment and the PyCharm compiler and (2) in 3D Slicer environment, as it is widely used by the neurology community, like the National Alliance for Medical Image Computing, among others. Weighted rotational invariant local operators were used in the calculus, namely average, standard deviation, coefficient of variation, normalized skewness, median, inter-quartile range and quartile coefficient of variation. Comparison between the implementations has been measured with normalized root-mean-square error. No differences have been observed for the non-linear parameters based on quartiles and errors smaller than 0.5% have been observed for operators that used Fast Fourier Transform based convolution instead of the explicit method. |
