GenTex stands for General Texture Analysis.
This package provides a suite of routines that combines standard texture analysis methods based on GLCM and entropy/statistical complexity measures.
GenTex provides a number of the standard algorithms required for generating complexity/texture measure estimates from multimodal imaging data. These include:
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Generation of multidimensional feature spaces from multimodal 'image' data (i.e. multiple 'co-registered' 1,2,3, or 4 dimensional data sets, e.g. multiple 'co-registered' time series, multimodal image data, space/time data..) via the use of a set of image templates, including:
- single voxels
- linear sequences in cardinal directions
- notches in cardinal directions
- light cones in cardinal directions and 45 degree angles
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Clustering methods for generating discrete ('grey') levels from the constructed feature space (the levels are then typically mapped to the original image space at the anchor points of the templates)
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Building co-occurrence matrices from a discrete level 'image' or a pair of discrete level 'images', where the discrete level 'images' are typically generated via feature space clustering of the original multimodal data sets (time series, images, space/time data...)
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Estimation of various complexity/texture measures from the co-occurrence matrices. (Haralick measures and epsilon machine related quantities) such as:
- CM Entropy
- EM Entropy
- Statistical Complexity
- Energy Uniformity
- Maximum Probability
- Contrast
- Inverse Difference Moment
- Correlation
- Probability of Run Length
- Epsilon Machine Run Length
- Run Length Asymmetry
- Homogeneity
- Cluster Tendency
- Multifractal Spectrum Energy Range
- Multifractal Spectrum Entropy Range
The documentation on GenTex in hosted here
pip install gentex
Note: It may require that a C compiler (e.g. gcc
) is installed on your system
- Karl Young (original developer)
- Nicolas Pannetier
- Norbert Schuff
GenTex is licensed under the terms of the BSD license. Please see the License file in the GenTex distribution
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K. Young, Y. Chen, J. Kornak, G. B. Matson, N. Schuff, 'Summarizing complexity in high dimensions', Phys Rev Lett. (2005) Mar 11;94(9):098701.
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C. R. Shalizi and J. P. Crutchfield, 'Computational Mechanics: Pattern and Prediction, Structure and Simplicity', Journal of Statistical Physics 104 (2001) 819--881.
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K. Young and J. P. Crutchfield, 'Fluctuation Spectroscopy', Chaos, Solitons, and Fractals 4 (1993) 5-39.
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J. P. Crutchfield and K. Young, 'Computation at the Onset of Chaos', in Entropy, Complexity, and Physics of Information, W. Zurek, editor, SFI Studies in the Sciences of Complexity, VIII, Addison-Wesley, Reading, Massachusetts (1990) 223-269.
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C. R. Shalizi and J. P. Crutchfield, 'Computational Mechanics: Pattern and Prediction, Structure and Simplicity', Journal of Statistical Physics 104 (2001) 819--881.