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首页> 外文会议>2017 XLIII Latin American Computer Conference >Python implementation of local intervoxel-texture operators in neuroimaging using Anaconda and 3D Slicer environments
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Python implementation of local intervoxel-texture operators in neuroimaging using Anaconda and 3D Slicer environments

机译：使用Anaconda和3D Slicer环境在神经成像中使用局部Intervoxel纹理运算符的Python实现

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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.
机译：在神经影像学中，磁共振图像可用于定位并获得各种参数，以发现广泛的病理状况，从而改善诊断并因此进行早期治疗。由于大脑的图像是立体的，因此将它们在体素中进行体积处理，而不是在像素中进行平面处理。我们提出了一种基于局部邻域的纹理参数的替代实现方法，该方法最近被证明可以使用扩散张量成像技术改善健康患者与患有阿尔茨海默氏病的患者之间大脑差异的检测[1]。我们使用Python的Anaconda环境和PyCharm编译器实现了方法（1），以及在3D Slicer环境中实现了方法（2），因为该方法已被神经病学界广泛使用，例如美国国家医学图像计算联盟。在演算中使用加权旋转不变局部算子，即平均值，标准差，变异系数，归一化偏度，中位数，四分位数间距和四分位数变异系数。实现之间的比较已通过归一化均方根误差进行了衡量。对于基于四分位数的非线性参数，未观察到差异，对于使用基于快速傅立叶变换的卷积代替显式方法的运算符，观察到的误差小于0.5％。

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