Cancer Imaging Phenomics Toolkit (CaPTk)  1.6.1
Further Application Details and Assumptions

Contents

Image Visualization

The visualization of images is based on the physical coordinate system of each image (i.e., the origin and direction information from within the image file is used for rendering). In practice, use of a consistent coordinate framework results in images with different origins to appear misaligned (shifted) when compared to other neuro-imaging software packages that do rendering based on the Cartesian coordinate information in the image.

CaPTk has been optimized for monitors with 16:9 resolution, especially 1920x1080 at 100% scaling. More resolutions and scaling options are being actively tested and support will increase in subsequent releases.


Extracted Features

Feature Family Specific
Features
Parameter
Name
Range Default Description, Formula and Comments
Intensity Features
(First-Order Statistics)
  • Minimum
  • Maximum
  • Mean
  • Standard Deviation
  • Variance
  • Skewness
  • Kurtosis
N.A. N.A. N.A.
  • Minimum Intensity = \( Min (I_{k}). \) where \( I_{k} \) is the intensity of pixel or voxel at index k.
  • Maximum Intensity = \( Max (I_{k}). \) where \( I_{k} \) is the intensity of pixel or voxel at index k.
  • Mean= \( \frac{\sum(X_{i})}{N} \) where N is the number of voxels/pixels.
  • Standard Deviation = \( \sqrt{\frac{\sum(X-\mu)^{2}}{N}}\) where \(\mu\) is the mean of the data.
  • Variance = \( \frac{\sum(X-\mu)^{2}}{N} \) where \(\mu\) is the mean intensity.
  • Skewness = \( \frac{\sum_{i=1}^{N}(X_{i} - \bar{X})^{3}/N} {s^{3}} \) where \(\bar{X}\) is the mean, s is the standard deviation and N is the number of pixels/voxels.
  • Kurtosis = \( \frac{\sum_{i=1}^{N}(X_{i} - \bar{X})^{4}/N}{s^{4}} \) where \(\bar{X}\) is the mean, s is the standard deviation and N is the number of pixels/voxels.
All features in this family are extracted from the raw intensities.
Histogram
-based
  • Bin Frequency
Num_Bins N.A. 10
  • Uses number of bins as input and the number of pixels in each bin would be the output.
All features in this family are extracted from the discretized intensities.
Volumetric
  • Volume/Area
Dimensions
Axis
2D:3D
x,y,z
3D
z
  • Volume/Area (depending on image dimension) and number of voxels/pixels in the ROI.
Morphologic
  • Elongation
  • Perimeter
  • Roundness
  • Eccentricity
Dimensions
Axis
2D:3D
x,y,z
3D
z
  • Elongation = \( \sqrt{\frac{i_{2}}{i_{1}}} \) where i_{n} are the second moments of particle around its principal axes.
  • Perimeter = \( 2 \pi r \) where r is the radius of the circle enclosing the shape.
  • Roundness = \( As/Ac = (Area of a shape)/(Area of circle) \) where circle has the same perimeter.
  • Eccentricity = \( \sqrt{1 - \frac{a*b}{c^{2}}} \) where c is the longest semi-principal axis of an ellipsoid fitted on an ROI, and a and b are the 2nd and 3rd longest semi-principal axes of the ellipsoid.
Local Binary
Pattern (LBP)
Radius
Neighborhood
N.A.
2:4:8
N.A.
8
  • The LBP codes are computed using N sampling points on a circle of radius R and using mapping table.
Grey Level
Co-occurrence
Matrix
(GLCM)
  • Energy (Angular Second Moment)
  • Contrast (Inertia)
  • Joint Entropy
  • Homogeneity (Inverse Difference Moment)
  • Correlation
  • Variance
  • SumAverage
  • Variance
  • Auto
    Correlation
Num_Bins

Num_Directions

Radius

Dimensions

Offset

Axis
N.A.

3:13

N.A.

2D:3D

Individual/Average/Combined

x,y,z
10

13

2

3D

Average

z
For a given image, a Grey Level Co-occurrence Matrix is created and \( g(i,j) \) represents an element in matrix
  • Energy = \( \sum_{i,j}g(i, j)^2 \)
  • Contrast = \( \sum_{i,j}(i - j)^2g(i, j) \)
  • Joint Entropy = \( -\sum_{i,j}g(i, j) \log_2 g(i, j) \)
  • Homogeneity = \( \sum_{i,j}\frac{1}{1 + (i - j)^2}g(i, j) \)
  • Correlation = \( \sum_{i,j}\frac{(i - \mu)(j - \mu)g(i, j)}{\sigma^2} \)
  • Sum Average = \( \sum_{i,j}i \cdot g(i, j) = \sum_{i,j}j \cdot g(i, j)\)(due to matrix symmetry)
  • Variance = \( \sum_{i,j}(i - \mu)^2 \cdot g(i, j) = \sum_{i,j}(j - \mu)^2 \cdot g(i, j)\) (due to matrix symmetry)
  • AutoCorrelation = \(\frac{\sum_{i,j}(i, j) g(i, j)-\mu_t^2}{\sigma_t^2}\) where \(\mu_t\) and \(\sigma_t\) are the mean and standard deviation of the row (or column, due to symmetry) sums.
All features are estimated within the ROI in an image, considering 26-connected neighboring voxels in the 3D volume. Note that the creation of the GLCM and its corresponding aforementioned features for all offsets are calculated using an existing ITK filter. The Individual option gives features for each individual offset, Average estimates the average across all offsets and assigns a single value for each feature and Combined combines the GLCM matrices generated across offsets and calculates a single set of features from this matrix.
Grey Level
Run-Length
Matrix
(GLRLM)
  • SRE
  • LRE
  • GLN
  • RLN
  • LGRE
  • HGRE
  • SRLGE
  • SRHGE
  • LRLGE
  • LRHGE
Num_Bins

Num_Directions

Radius

Dimensions

Axis

Offset

Distance_Range
N.A.

3:13


N.A.

2D:3D

x,y,z

Individual/Average/Combined

1:5
10

13


2

3D

z

Average

1
For a given image, a run-length matrix \( P(i; j)\) is defined as the number of runs with pixels of gray level i and run length j.
  • Short Run Emphasis (SRE) = \( \frac{1}{n_r}\sum_{i,j}^{N}\frac{p(i,j)}{j^2} \)
  • Long Run Emphasis (LRE) = \( \frac{1}{n_r}\sum_{j}^{N}p(i,j) \cdot j^2\)
  • Grey Level Non-uniformity (GLN) = \( \frac{1}{n_r}\sum_{i}^{M}\Big(\sum_{j}^{N}p(i,j) \Big)^2 \)
  • Run Length Non-uniformity (RLN) = \( \frac{1}{n_r}\sum_{j}^{N}\Big(\sum_{i}^{M}p(i,j) \Big)^2 \)
  • Low Grey-Level Run Emphasis (LGRE)= \( \frac{1}{n_r}\sum_{i}^{M}\frac{p_g(i)}{i^2} \)
  • High Grey-Level Run Emphasis (HGRE)= \( \frac{1}{n_r}\sum_{i}^{M}p_g(i) \cdot i^2 \)
  • Short Run Low Grey-Level Emphasis (SRLGE)= \(\frac{1}{n_r}\sum_{i}^{M}\sum_{j}^{N}\frac{p(i,j)}{i^2 \cdot j^2} \)
  • Short Run High Grey-Level Emphasis (SRLGE) = \( \frac{1}{n_r}\sum_{i}^{M}\sum_{j}^{N}\frac{p(i,j) \cdot i^2 }{j^2}\)
  • Long Run Low Grey-Level Emphasis (LRLGE) = \( \frac{1}{n_r}\sum_{i}^{M}\sum_{j}^{N}\frac{p(i,j) \cdot j^2 }{i^2} \)
  • Long Run High Grey-Level Emphasis (LRHGE) = \( \frac{1}{n_r}\sum_{i}^{M}\sum_{j}^{N}p(i,j) \cdot i^2 \cdot j^2 \)
All features are estimated within the ROI in an image, considering 26-connected neighboring voxels in the 3D volume. Note that the creation of the GLRLM and its corresponding aforementioned features for all offsets are calculated using an existing ITK filter. The Individual option gives features for each individual offset, Average estimates the average across all offsets and assigns a single value for each feature and Combined combines the GLRLM matrices generated across offsets and calculates a single set of features from this matrix.
Neighborhood
Grey-Tone
Difference
Matrix
(NGTDM)
  • Coarseness
  • Contrast
  • Busyness
  • Complexity
  • Strength
Num_Bins

Num_Directions

Dimensions

Axis

Distance_Range
N.A.

3:13


2D:3D

x,y,z

1:5
10

13


3D

N.A.

1
  • Coarseness = \( \Big[ \epsilon + \sum_{i=0}^{G_{k}} p_{i}s(i) \Big]\)
  • Contrast = \( \Big[\frac{1}{N_{s}(N_{s}-1)}\sum_{i}^{G_{k}}\sum_{j}^{G_{k}}p_{i}p_{j}(i-j)^2\Big]\Big[\frac{1}{n^2}\sum_{i}^{G_{k}}s(i)\Big] \)
  • Busyness = \( \Big[\sum_{i}^{G_{k}}p_{i}s(i)\Big]\Big/ \Big[\sum_{i}^{G_{k}}\sum_{j}^{G_{k}}i p_{i} - j p_{j}\Big] \)
  • Complexity = \( \sum_{i}^{G_{k}}\sum_{j}^{G_{k}} \Big[ \frac{(|i-j|)}{(n^{2}(p_{i}+p_{j}))} \Big] \Big[ p_{i}s(i)+p_{j}s(j) \Big]\)
  • Strength = \( \Big[\sum_{i}^{G_{k}}\sum_{j}^{G_{k}}(p_{i}+p_{j})(i-j)^{2}\Big]/\Big[\epsilon + \sum_{i}^{G_{k}} s(i)\Big]\)
Where \(p_{i}\) is the probability of occurrence of a voxel of intensity i and \(s(i)\) represents the NGTDM value of intensity i calculated as: \( \sum │i - Ai│\). Ai indicates the average intensity of the surrounding voxels without including the central voxel.
Grey Level
Size-Zone
Matrix
(GLSZM)
  • SZE
  • LZE
  • GLN
  • ZSN
  • ZP
  • LGZE
  • HGZE
  • SZLGE
  • SZHGE
  • LZLGE
  • LZHGE
  • GLV
  • ZLV
Num_Bins

Num_Directions

Radius

Dimensions

Axis

Distance_Range
N.A.

3:13


N.A.

2D:3D

x,y,z

1:5
10

13


2

3D

z

4
For a given image, a run-length matrix \( P(i; j)\) is defined as the number of runs with pixels of gray level i and run length j.
  • Small Zone Emphasis (SZE) = \( \frac{1}{n_r}\sum_{i,j}^{N}\frac{p(i,j)}{j^2} \)
  • Large Zone Emphasis(LZE) = \( \frac{1}{n_r}\sum_{j}^{N}p(i,j) \cdot j^2\)
  • Gray-Level Non-uniformity (GLN) = \( \frac{1}{n_r}\sum_{i}^{M}\Big(\sum_{j}^{N}p(i,j) \Big)^2 \)
  • Zone-Size Non-uniformity (ZSN) = \( \frac{1}{n_r}\sum_{j}^{N}\Big(\sum_{i}^{M}p(i,j) \Big)^2 \)
  • Zone Percentage (ZP) = \( \frac{n_{r}}{n_p} \) where \( n_r \) is the total number of runs and \( n_p \) is the number of pixels in the image.
  • Low Grey-Level Zone Emphasis (LGZE)= \( \frac{1}{n_r}\sum_{i}^{M}\frac{p_g(i)}{i^2} \)
  • High Grey-Level Zone Emphasis (HGZE)= \( \frac{1}{n_r}\sum_{i}^{M}p_g(i) \cdot i^2 \)
  • Short Zone Low Grey-Level Emphasis (SZLGE)= \(\frac{1}{n_r}\sum_{i}^{M}\sum_{j}^{N}\frac{p(i,j)}{i^2 \cdot j^2} \)
  • Short Zone High Grey-Level Emphasis (SZLGE) = \( \frac{1}{n_r}\sum_{i}^{M}\sum_{j}^{N}\frac{p(i,j) \cdot i^2 }{j^2}\)
  • Long Zone Low Grey-Level Emphasis (LZLGE) = \( \frac{1}{n_r}\sum_{i}^{M}\sum_{j}^{N}\frac{p(i,j) \cdot j^2 }{i^2} \)
  • Long Zone High Grey-Level Emphasis (LZHGE) = \( \frac{1}{n_r}\sum_{i}^{M}\sum_{j}^{N}p(i,j) \cdot i^2 \cdot j^2 \)
All features are estimated within the ROI in an image, considering 26-connected neighboring voxels in the 3D volume.

The parameterization of the lattice-based strategy for feature extraction is defined by:

  • The grid spacing representing the distance between consecutive lattice points (Default: 6.3mm).
  • The size of the local region centered at each lattice point (Default: 6.3mm).