diff --git a/src/main/resources/schemas/radm/radm.xsd b/src/main/resources/schemas/radm/radm.xsd index 35a2dca..2ab6ab8 100644 --- a/src/main/resources/schemas/radm/radm.xsd +++ b/src/main/resources/schemas/radm/radm.xsd @@ -1,656 +1,663 @@ - - + + - - - - - - - - - Energy is a measure of the magnitude of voxel values in an image. A larger values implies a greater sum of the squares of these values. - - - - - - Total Energy is the value of Energy feature scaled by the volume of the voxel in cubic mm. Not present in IBSI feature definitions. - - - - - - Entropy specifies the uncertainty/randomness in the image values. It measures the average amount of information required to encode the image values. Defined by IBSI as Intensity Histogram Entropy. - - - - - - - - - - - - The maximum gray level intensity within the ROI. - - - - - - The average gray level intensity within the ROI. - - - - - - The median gray level intensity within the ROI. - - - - - - - - The range of gray values in the ROI. - - - - - - Mean Absolute Deviation is the mean distance of all intensity values from the Mean Value of the image array. - - - - - - Robust Mean Absolute Deviation is the mean distance of all intensity values from the Mean Value calculated on the subset of image array with gray levels in between, or equal to the 10th and 90th percentile. - - - - - - RMS is the square-root of the mean of all the squared intensity values. It is another measure of the magnitude of the image values. This feature is volume-confounded, a larger value of c increases the effect of volume-confounding. - - - - - - Standard Deviation measures the amount of variation or dispersion from the Mean Value. - - - - - - Skewness measures the asymmetry of the distribution of values about the Mean value. Depending on where the tail is elongated and the mass of the distribution is concentrated, this value can be positive or negative. Related links: https://en.wikipedia.org/wiki/Skewness In case of a flat region, the standard deviation and 4rd central moment will be both 0. In this case, a value of 0 is returned. - - - - - - Kurtosis is a measure of the ‘peakedness’ of the distribution of values in the image ROI. A higher kurtosis implies that the mass of the distribution is concentrated towards the tail(s) rather than towards the mean. A lower kurtosis implies the reverse: that the mass of the distribution is concentrated towards a spike near the Mean value. Related links: https://en.wikipedia.org/wiki/Kurtosis In case of a flat region, the standard deviation and 4rd central moment will be both 0. In this case, a value of 0 is returned. - - - - - - Variance is the the mean of the squared distances of each intensity value from the Mean value. This is a measure of the spread of the distribution about the mean. - - - - - - Uniformity is a measure of the sum of the squares of each intensity value. This is a measure of the heterogeneity of the image array, where a greater uniformity implies a greater heterogeneity or a greater range of discrete intensity values. Defined by IBSI as Intensity Histogram Uniformity. - - - - - - - - - - - - The volume of the ROI is approximated by multiplying the number of voxels in the ROI by the volume of a single voxel. - - - - - - Surface Area is an approximation of the surface of the ROI in mm2, calculated using a marching cubes algorithm. - - - - - - Here, a lower value indicates a more compact (sphere-like) shape. This feature is not dimensionless, and is therefore (partly) dependent on the volume of the ROI. - - - - - - Sphericity is a measure of the roundness of the shape of the tumor region relative to a sphere. It is a dimensionless measure, independent of scale and orientation. The value range is greater than 0 and less than or equal to 1, where a value of 1 indicates a perfect sphere (a sphere has the smallest possible surface area for a given volume, compared to other solids). - - - - - - Similar to Sphericity, Compactness 1 is a measure of how compact the shape of the tumor is relative to a sphere (most compact). It is therefore correlated to Sphericity and redundant. - - - - - - Similar to Sphericity and Compactness 1, Compactness 2 is a measure of how compact the shape of the tumor is relative to a sphere (most compact). It is a dimensionless measure, independent of scale and orientation. - - - - - - Spherical Disproportion is the ratio of the surface area of the tumor region to the surface area of a sphere with the same volume as the tumor region, and by definition, the inverse of Sphericity. Therefore, the value range is spherical disproportion greater than or equal to 1, with a value of 1 indicating a perfect sphere. - - - - - - Maximum 3D diameter is defined as the largest pairwise Euclidean distance between surface voxels in the ROI. Also known as Feret Diameter. - - - - - - Maximum 2D diameter (Slice) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-column (generally the axial) plane. - - - - - - Maximum 2D diameter (Column) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-slice (usually the coronal) plane. - - - - - - Maximum 2D diameter (Row) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the column-slice (usually the sagittal) plane. - - - - - - - - - - - - Elongation is calculated using its implementation in SimpleITK. The values range between 1 (where the cross section through the first and second largest principal moments is circle-like (non-elongated)) and 0 (where the object is a single point or 1 dimensional line). - - - - - - Flatness is calculated using its implementation in SimpleITK. The values range between 1 (non-flat, sphere-like) and 0 (a flat object). - - - - - - - - - - - - Autocorrelation is a measure of the magnitude of the fineness and coarseness of texture. - - - - - - - - Cluster Prominence is a measure of the skewness and asymmetry of the GLCM. A higher values implies more asymmetry about the mean while a lower value indicates a peak near the mean value and less variation about the mean. - - - - - - Cluster Shade is a measure of the skewness and uniformity of the GLCM. A higher cluster shade implies greater asymmetry about the mean. - - - - - - Cluster Tendency is a measure of groupings of voxels with similar gray-level values. - - - - - - Contrast is a measure of the local intensity variation, favoring values away from the diagonal. A larger value correlates with a greater disparity in intensity values among neighboring voxels. - - - - - - Correlation is a value between 0 (uncorrelated) and 1 (perfectly correlated) showing the linear dependency of gray level values to their respective voxels in the GLCM. - - - - - - Difference Average measures the relationship between occurrences of pairs with similar intensity values and occurrences of pairs with differing intensity values. - - - - - - Difference Entropy is a measure of the randomness/variability in neighborhood intensity value differences. - - - - - - Difference Variance is a measure of heterogeneity that places higher weights on differing intensity level pairs that deviate more from the mean. - - - - - - Energy is a measure of homogeneous patterns in the image. A greater Energy implies that there are more instances of intensity value pairs in the image that neighbor each other at higher frequencies. Defined by IBSI as Angular Second Moment. - - - - - - Joint entropy is a measure of the randomness/variability in neighborhood intensity values. Defined by IBSI as Joint entropy. - - - - - - - - - - IDM (a.k.a Homogeneity 2) is a measure of the local homogeneity of an image. IDM weights are the inverse of the Contrast weights (decreasing exponentially from the diagonal in the GLCM). - - - - - - IDMN (inverse difference moment normalized) is a measure of the local homogeneity of an image. IDMN weights are the inverse of the Contrast weights (decreasing exponentially from the diagonal in the GLCM). Unlike Homogeneity2, IDMN normalizes the square of the difference between neighboring intensity values by dividing over the square of the total number of discrete intensity values. - - - - - - ID (a.k.a. Homogeneity 1) is another measure of the local homogeneity of an image. With more uniform gray levels, the denominator will remain low, resulting in a higher overall value. - - - - - - IDN (inverse difference normalized) is another measure of the local homogeneity of an image. Unlike Homogeneity1, IDN normalizes the difference between the neighboring intensity values by dividing over the total number of discrete intensity values. - - - - - - - - Maximum Probability is occurrences of the most predominant pair of neighboring intensity values. Defined by IBSI as Joint maximum. - - - - - - Sum Average measures the relationship between occurrences of pairs with lower intensity values and occurrences of pairs with higher intensity values. - - - - - - Sum Entropy is a sum of neighborhood intensity value differences. - - - - - - Sum of Squares or Variance is a measure in the distribution of neigboring intensity level pairs about the mean intensity level in the GLCM. Defined by IBSI as Joint Variance. - - - - - - SAE is a measure of the distribution of small size zones, with a greater value indicative of more smaller size zones and more fine textures. - - - - - - LAE is a measure of the distribution of large area size zones, with a greater value indicative of more larger size zones and more coarse textures. - - - - - - GLN measures the variability of gray-level intensity values in the image, with a lower value indicating more homogeneity in intensity values. - - - - - - GLNN measures the variability of gray-level intensity values in the image, with a lower value indicating a greater similarity in intensity values. This is the normalized version of the GLN formula. - - - - - - SZN measures the variability of size zone volumes in the image, with a lower value indicating more homogeneity in size zone volumes. - - - - - - SZNN measures the variability of size zone volumes throughout the image, with a lower value indicating more homogeneity among zone size volumes in the image. This is the normalized version of the SZN formula. - - - - - - ZP measures the coarseness of the texture by taking the ratio of number of zones and number of voxels in the ROI. - - - - - - GLV measures the variance in gray level intensities for the zones. - - - - - - ZV measures the variance in zone size volumes for the zones. - - - - - - ZE measures the uncertainty/randomness in the distribution of zone sizes and gray levels. A higher value indicates more heterogeneneity in the texture patterns. - - - - - - LGLZE measures the distribution of lower gray-level size zones, with a higher value indicating a greater proportion of lower gray-level values and size zones in the image. - - - - - - HGLZE measures the distribution of the higher gray-level values, with a higher value indicating a greater proportion of higher gray-level values and size zones in the image. - - - - - - SALGLE measures the proportion in the image of the joint distribution of smaller size zones with lower gray-level values. - - - - - - SAHGLE measures the proportion in the image of the joint distribution of smaller size zones with higher gray-level values. - - - - - - LALGLE measures the proportion in the image of the joint distribution of larger size zones with lower gray-level values. - - - - - - LAHGLE measures the proportion in the image of the joint distribution of larger size zones with higher gray-level values. - - - - - - - - - - - - SRE is a measure of the distribution of short run lengths, with a greater value indicative of shorter run lengths and more fine textural textures. - - - - - - LRE is a measure of the distribution of long run lengths, with a greater value indicative of longer run lengths and more coarse structural textures. - - - - - - GLN measures the similarity of gray-level intensity values in the image, where a lower GLN value correlates with a greater similarity in intensity values. - - - - - - GLNN measures the similarity of gray-level intensity values in the image, where a lower GLNN value correlates with a greater similarity in intensity values. This is the normalized version of the GLN formula. - - - - - - RLN measures the similarity of run lengths throughout the image, with a lower value indicating more homogeneity among run lengths in the image. - - - - - - RLNN measures the similarity of run lengths throughout the image, with a lower value indicating more homogeneity among run lengths in the image. This is the normalized version of the RLN formula. - - - - - - RP measures the coarseness of the texture by taking the ratio of number of runs and number of voxels in the ROI. - - - - - - GLV measures the variance in gray level intensity for the runs. - - - - - - RV is a measure of the variance in runs for the run lengths. - - - - - - RE measures the uncertainty/randomness in the distribution of run lengths and gray levels. A higher value indicates more heterogeneity in the texture patterns. - - - - - - LGLRE measures the distribution of low gray-level values, with a higher value indicating a greater concentration of low gray-level values in the image. - - - - - - HGLRE measures the distribution of the higher gray-level values, with a higher value indicating a greater concentration of high gray-level values in the image. - - - - - - SRLGLE measures the joint distribution of shorter run lengths with lower gray-level values. - - - - - - SRHGLE measures the joint distribution of shorter run lengths with higher gray-level values. - - - - - - LRLGLRE measures the joint distribution of long run lengths with lower gray-level values. - - - - - - LRHGLRE measures the joint distribution of long run lengths with higher gray-level values. - - - - - - - - - - - - Coarseness is a measure of average difference between the center voxel and its neighbourhood and is an indication of the spatial rate of change. A higher value indicates a lower spatial change rate and a locally more uniform texture. - - - - - - Contrast is a measure of the spatial intensity change, but is also dependent on the overall gray level dynamic range. Contrast is high when both the dynamic range and the spatial change rate are high, i.e. an image with a large range of gray levels, with large changes between voxels and their neighbourhood. - - - - - - A measure of the change from a pixel to its neighbour. A high value for busyness indicates a ‘busy’ image, with rapid changes of intensity between pixels and its neighbourhood. - - - - - - An image is considered complex when there are many primitive components in the image, i.e. the image is non-uniform and there are many rapid changes in gray level intensity. - - - - - - Strenght is a measure of the primitives in an image. Its value is high when the primitives are easily defined and visible, i.e. an image with slow change in intensity but more large coarse differences in gray level intensities. - - - - - - - - - - - - A measure of the distribution of small dependencies, with a greater value indicative of smaller dependence and less homogeneous textures. - - - - - - A measure of the distribution of large dependencies, with a greater value indicative of larger dependence and more homogeneous textures. - - - - - - Measures the similarity of gray-level intensity values in the image, where a lower GLN value correlates with a greater similarity in intensity values. - - - - - - Measures the similarity of dependence throughout the image, with a lower value indicating more homogeneity among dependencies in the image. - - - - - - Measures the similarity of dependence throughout the image, with a lower value indicating more homogeneity among dependencies in the image. This is the normalized version of the DLN formula. - - - - - - Measures the variance in grey level in the image. - - - - - - Measures the variance in dependence size in the image. - - - - - - - - Measures the distribution of low gray-level values, with a higher value indicating a greater concentration of low gray-level values in the image. - - - - - - Measures the distribution of the higher gray-level values, with a higher value indicating a greater concentration of high gray-level values in the image. - - - - - - Measures the joint distribution of small dependence with lower gray-level values. - - - - - - Measures the joint distribution of small dependence with higher gray-level values. - - - - - - Measures the joint distribution of large dependence with lower gray-level values. - - - - - - Measures the joint distribution of large dependence with higher gray-level values. - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Energy is a measure of the magnitude of voxel values in an image. A larger values implies a greater sum of the squares of these values. + + + + + Total Energy is the value of Energy feature scaled by the volume of the voxel in cubic mm. Not present in IBSI feature definitions. + + + + + Entropy specifies the uncertainty/randomness in the image values. It measures the average amount of information required to encode the image values. Defined by IBSI as Intensity Histogram Entropy. + + + + + + + + The maximum gray level intensity within the ROI. + + + + + The average gray level intensity within the ROI. + + + + + The median gray level intensity within the ROI. + + + + + + The range of gray values in the ROI. + + + + + Mean Absolute Deviation is the mean distance of all intensity values from the Mean Value of the image array. + + + + + Robust Mean Absolute Deviation is the mean distance of all intensity values from the Mean Value calculated on the subset of image array with gray levels in between, or equal to the 10th and 90th percentile. + + + + + RMS is the square-root of the mean of all the squared intensity values. It is another measure of the magnitude of the image values. This feature is volume-confounded, a larger value of c increases the effect of volume-confounding. + + + + + Standard Deviation measures the amount of variation or dispersion from the Mean Value. + + + + + Skewness measures the asymmetry of the distribution of values about the Mean value. Depending on where the tail is elongated and the mass of the distribution is concentrated, this value can be positive or negative. Related links: https://en.wikipedia.org/wiki/Skewness In case of a flat region, the standard deviation and 4rd central moment will be both 0. In this case, a value of 0 is returned. + + + + + Kurtosis is a measure of the ‘peakedness’ of the distribution of values in the image ROI. A higher kurtosis implies that the mass of the distribution is concentrated towards the tail(s) rather than towards the mean. A lower kurtosis implies the reverse: that the mass of the distribution is concentrated towards a spike near the Mean value. Related links: https://en.wikipedia.org/wiki/Kurtosis In case of a flat region, the standard deviation and 4rd central moment will be both 0. In this case, a value of 0 is returned. + + + + + Variance is the the mean of the squared distances of each intensity value from the Mean value. This is a measure of the spread of the distribution about the mean. + + + + + Uniformity is a measure of the sum of the squares of each intensity value. This is a measure of the heterogeneity of the image array, where a greater uniformity implies a greater heterogeneity or a greater range of discrete intensity values. Defined by IBSI as Intensity Histogram Uniformity. + + + + + + + + + + + The volume of the ROI is approximated by multiplying the number of voxels in the ROI by the volume of a single voxel. + + + + + Surface Area is an approximation of the surface of the ROI in mm2, calculated using a marching cubes algorithm. + + + + + Here, a lower value indicates a more compact (sphere-like) shape. This feature is not dimensionless, and is therefore (partly) dependent on the volume of the ROI. + + + + + Sphericity is a measure of the roundness of the shape of the tumor region relative to a sphere. It is a dimensionless measure, independent of scale and orientation. The value range is greater than 0 and less than or equal to 1, where a value of 1 indicates a perfect sphere (a sphere has the smallest possible surface area for a given volume, compared to other solids). + + + + + Similar to Sphericity, Compactness 1 is a measure of how compact the shape of the tumor is relative to a sphere (most compact). It is therefore correlated to Sphericity and redundant. + + + + + Similar to Sphericity and Compactness 1, Compactness 2 is a measure of how compact the shape of the tumor is relative to a sphere (most compact). It is a dimensionless measure, independent of scale and orientation. + + + + + Spherical Disproportion is the ratio of the surface area of the tumor region to the surface area of a sphere with the same volume as the tumor region, and by definition, the inverse of Sphericity. Therefore, the value range is spherical disproportion greater than or equal to 1, with a value of 1 indicating a perfect sphere. + + + + + Maximum 3D diameter is defined as the largest pairwise Euclidean distance between surface voxels in the ROI. Also known as Feret Diameter. + + + + + Maximum 2D diameter (Slice) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-column (generally the axial) plane. + + + + + Maximum 2D diameter (Column) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-slice (usually the coronal) plane. + + + + + Maximum 2D diameter (Row) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the column-slice (usually the sagittal) plane. + + + + + + + + Elongation is calculated using its implementation in SimpleITK. The values range between 1 (where the cross section through the first and second largest principal moments is circle-like (non-elongated)) and 0 (where the object is a single point or 1 dimensional line). + + + + + Flatness is calculated using its implementation in SimpleITK. The values range between 1 (non-flat, sphere-like) and 0 (a flat object). + + + + + + + + + + + Autocorrelation is a measure of the magnitude of the fineness and coarseness of texture. + + + + + + Cluster Prominence is a measure of the skewness and asymmetry of the GLCM. A higher values implies more asymmetry about the mean while a lower value indicates a peak near the mean value and less variation about the mean. + + + + + Cluster Shade is a measure of the skewness and uniformity of the GLCM. A higher cluster shade implies greater asymmetry about the mean. + + + + + Cluster Tendency is a measure of groupings of voxels with similar gray-level values. + + + + + Contrast is a measure of the local intensity variation, favoring values away from the diagonal. A larger value correlates with a greater disparity in intensity values among neighboring voxels. + + + + + Correlation is a value between 0 (uncorrelated) and 1 (perfectly correlated) showing the linear dependency of gray level values to their respective voxels in the GLCM. + + + + + Difference Average measures the relationship between occurrences of pairs with similar intensity values and occurrences of pairs with differing intensity values. + + + + + Difference Entropy is a measure of the randomness/variability in neighborhood intensity value differences. + + + + + Difference Variance is a measure of heterogeneity that places higher weights on differing intensity level pairs that deviate more from the mean. + + + + + Energy is a measure of homogeneous patterns in the image. A greater Energy implies that there are more instances of intensity value pairs in the image that neighbor each other at higher frequencies. Defined by IBSI as Angular Second Moment. + + + + + Joint entropy is a measure of the randomness/variability in neighborhood intensity values. Defined by IBSI as Joint entropy. + + + + + + + IDM (a.k.a Homogeneity 2) is a measure of the local homogeneity of an image. IDM weights are the inverse of the Contrast weights (decreasing exponentially from the diagonal in the GLCM). + + + + + IDMN (inverse difference moment normalized) is a measure of the local homogeneity of an image. IDMN weights are the inverse of the Contrast weights (decreasing exponentially from the diagonal in the GLCM). Unlike Homogeneity2, IDMN normalizes the square of the difference between neighboring intensity values by dividing over the square of the total number of discrete intensity values. + + + + + ID (a.k.a. Homogeneity 1) is another measure of the local homogeneity of an image. With more uniform gray levels, the denominator will remain low, resulting in a higher overall value. + + + + + IDN (inverse difference normalized) is another measure of the local homogeneity of an image. Unlike Homogeneity1, IDN normalizes the difference between the neighboring intensity values by dividing over the total number of discrete intensity values. + + + + + + Maximum Probability is occurrences of the most predominant pair of neighboring intensity values. Defined by IBSI as Joint maximum. + + + + + Sum Average measures the relationship between occurrences of pairs with lower intensity values and occurrences of pairs with higher intensity values. + + + + + Sum Entropy is a sum of neighborhood intensity value differences. + + + + + Sum of Squares or Variance is a measure in the distribution of neigboring intensity level pairs about the mean intensity level in the GLCM. Defined by IBSI as Joint Variance. + + + + + SAE is a measure of the distribution of small size zones, with a greater value indicative of more smaller size zones and more fine textures. + + + + + LAE is a measure of the distribution of large area size zones, with a greater value indicative of more larger size zones and more coarse textures. + + + + + GLN measures the variability of gray-level intensity values in the image, with a lower value indicating more homogeneity in intensity values. + + + + + GLNN measures the variability of gray-level intensity values in the image, with a lower value indicating a greater similarity in intensity values. This is the normalized version of the GLN formula. + + + + + SZN measures the variability of size zone volumes in the image, with a lower value indicating more homogeneity in size zone volumes. + + + + + SZNN measures the variability of size zone volumes throughout the image, with a lower value indicating more homogeneity among zone size volumes in the image. This is the normalized version of the SZN formula. + + + + + ZP measures the coarseness of the texture by taking the ratio of number of zones and number of voxels in the ROI. + + + + + GLV measures the variance in gray level intensities for the zones. + + + + + ZV measures the variance in zone size volumes for the zones. + + + + + ZE measures the uncertainty/randomness in the distribution of zone sizes and gray levels. A higher value indicates more heterogeneneity in the texture patterns. + + + + + LGLZE measures the distribution of lower gray-level size zones, with a higher value indicating a greater proportion of lower gray-level values and size zones in the image. + + + + + HGLZE measures the distribution of the higher gray-level values, with a higher value indicating a greater proportion of higher gray-level values and size zones in the image. + + + + + SALGLE measures the proportion in the image of the joint distribution of smaller size zones with lower gray-level values. + + + + + SAHGLE measures the proportion in the image of the joint distribution of smaller size zones with higher gray-level values. + + + + + LALGLE measures the proportion in the image of the joint distribution of larger size zones with lower gray-level values. + + + + + LAHGLE measures the proportion in the image of the joint distribution of larger size zones with higher gray-level values. + + + + + + + + + + + SRE is a measure of the distribution of short run lengths, with a greater value indicative of shorter run lengths and more fine textural textures. + + + + + LRE is a measure of the distribution of long run lengths, with a greater value indicative of longer run lengths and more coarse structural textures. + + + + + GLN measures the similarity of gray-level intensity values in the image, where a lower GLN value correlates with a greater similarity in intensity values. + + + + + GLNN measures the similarity of gray-level intensity values in the image, where a lower GLNN value correlates with a greater similarity in intensity values. This is the normalized version of the GLN formula. + + + + + RLN measures the similarity of run lengths throughout the image, with a lower value indicating more homogeneity among run lengths in the image. + + + + + RLNN measures the similarity of run lengths throughout the image, with a lower value indicating more homogeneity among run lengths in the image. This is the normalized version of the RLN formula. + + + + + RP measures the coarseness of the texture by taking the ratio of number of runs and number of voxels in the ROI. + + + + + GLV measures the variance in gray level intensity for the runs. + + + + + RV is a measure of the variance in runs for the run lengths. + + + + + RE measures the uncertainty/randomness in the distribution of run lengths and gray levels. A higher value indicates more heterogeneity in the texture patterns. + + + + + LGLRE measures the distribution of low gray-level values, with a higher value indicating a greater concentration of low gray-level values in the image. + + + + + HGLRE measures the distribution of the higher gray-level values, with a higher value indicating a greater concentration of high gray-level values in the image. + + + + + SRLGLE measures the joint distribution of shorter run lengths with lower gray-level values. + + + + + SRHGLE measures the joint distribution of shorter run lengths with higher gray-level values. + + + + + LRLGLRE measures the joint distribution of long run lengths with lower gray-level values. + + + + + LRHGLRE measures the joint distribution of long run lengths with higher gray-level values. + + + + + + + + + + + Coarseness is a measure of average difference between the center voxel and its neighbourhood and is an indication of the spatial rate of change. A higher value indicates a lower spatial change rate and a locally more uniform texture. + + + + + Contrast is a measure of the spatial intensity change, but is also dependent on the overall gray level dynamic range. Contrast is high when both the dynamic range and the spatial change rate are high, i.e. an image with a large range of gray levels, with large changes between voxels and their neighbourhood. + + + + + A measure of the change from a pixel to its neighbour. A high value for busyness indicates a ‘busy’ image, with rapid changes of intensity between pixels and its neighbourhood. + + + + + An image is considered complex when there are many primitive components in the image, i.e. the image is non-uniform and there are many rapid changes in gray level intensity. + + + + + Strength is a measure of the primitives in an image. Its value is high when the primitives are easily defined and visible, i.e. an image with slow change in intensity but more large coarse differences in gray level intensities. + + + + + + + + + + + A measure of the distribution of small dependencies, with a greater value indicative of smaller dependence and less homogeneous textures. + + + + + A measure of the distribution of large dependencies, with a greater value indicative of larger dependence and more homogeneous textures. + + + + + Measures the similarity of gray-level intensity values in the image, where a lower GLN value correlates with a greater similarity in intensity values. + + + + + Measures the similarity of dependence throughout the image, with a lower value indicating more homogeneity among dependencies in the image. + + + + + Measures the similarity of dependence throughout the image, with a lower value indicating more homogeneity among dependencies in the image. This is the normalized version of the DLN formula. + + + + + Measures the variance in grey level in the image. + + + + + Measures the variance in dependence size in the image. + + + + + + Measures the distribution of low gray-level values, with a higher value indicating a greater concentration of low gray-level values in the image. + + + + + Measures the distribution of the higher gray-level values, with a higher value indicating a greater concentration of high gray-level values in the image. + + + + + Measures the joint distribution of small dependence with lower gray-level values. + + + + + Measures the joint distribution of small dependence with higher gray-level values. + + + + + Measures the joint distribution of large dependence with lower gray-level values. + + + + + Measures the joint distribution of large dependence with higher gray-level values. + + + + + + + + + + + Gray Level Non-Uniformity (GLN) measures the variability of gray-level intensity values in the image, with a lower value indicating more homogeneity in intensity values. + + + + + Gray Level Non-Uniformity Normalized (GLNN) measures the variability of gray-level intensity values in the image, with a lower value indicating a greater similarity in intensity values. This is the normalized version of the GLN formula. + + + + + Gray Level Variance (GLV) measures the variance in gray level intensities for the zones. + + + + + Large Area Emphasis (LAE) is a measure of the distribution of large area size zones, with a greater value indicative of more larger size zones and more coarse textures. + + + + + Large Area High Gray Level Emphasis (LAHGLE) measures the proportion in the image of the joint distribution of larger size zones with higher gray-level values. + + + + + Large Area Low Gray Level Emphasis (LALGLE) measures the proportion in the image of the joint distribution of larger size zones with lower gray-level values. + + + + + Low Gray Level Zone Emphasis (LGLZE) measures the distribution of lower gray-level size zones, with a higher value indicating a greater proportion of lower gray-level values and size zones in the image. + + + + + High Gray Level Zone Emphasis (HGLZE) measures the distribution of the higher gray-level values, with a higher value indicating a greater proportion of higher gray-level values and size zones in the image. + + + + + Size-Zone Non-Uniformity (SZN) measures the variability of size zone volumes in the image, with a lower value indicating more homogeneity in size zone volumes. + + + + + Size-Zone Non-Uniformity Normalized (SZNN) measures the variability of size zone volumes throughout the image, with a lower value indicating more homogeneity among zone size volumes in the image. This is the normalized version of the SZN formula. + + + + + Small Area Emphasis (SAE) is a measure of the distribution of small size zones, with a greater value indicative of more smaller size zones and more fine textures. + + + + + Small Area High Gray Level Emphasis (SAHGLE) measures the proportion in the image of the joint distribution of smaller size zones with higher gray-level values. + + + + + Small Area Low Gray Level Emphasis (SALGLE) measures the proportion in the image of the joint distribution of smaller size zones with lower gray-level values. + + + + + Zone Percentage (ZP) measures the coarseness of the texture by taking the ratio of number of zones and number of voxels in the ROI. + + + + + Zone Variance (ZV) measures the variance in zone size volumes for the zones. + + + + + Zone Entropy (ZE) measures the uncertainty/randomness in the distribution of zone sizes and gray levels. A higher value indicates more heterogeneneity in the texture patterns. + + + + + + + +