ilastik object features describe objects in terms of numbers. These are used in classification to differentiate between different types of objects (classes). Per default ilastik comes with 3 feature plugins: “Standard Object Features”, “Skeleton Features” (2D only), and “Convex Hull Features”.

A short overview of available object features is given in the following segment of the webinar ilastik beyond pixel classification - [NEUBIASAcademy@Home]:

Some practical advice on selecting features can be found in our i2k ilastik tutorial:

Following here is a list of all available object features along with their description.

Bounding Box Maximum

The coordinates of the upper right corner of the object's bounding box. The first axis is x, then y, then z (if available).

Bounding Box Minimum

The coordinates of the lower left corner of the object's bounding box. The first axis is x, then y, then z (if available).

Size in pixels

Total size of the object in pixels. No correction for anisotropic resolution or anything else.

Covariance of Channel Intensity

For multi-channel images this feature computes the covariance between the channels inside the object.

Covariance of Channel Intensity in neighborhood

For multi-channel images this feature computes the covariance between the channels in the object neighborhood. The size of the neighborhood is determined from the controls in the lower part of the dialogue.

Histogram of Intensity

Histogram of the intensity distribution inside the object. The histogram has 64 bins and its range is computed from the global minimum and maximum intensity values in the whole image.

Histogram of Intensity in neighborhood

Histogram of the intensity distribution in the object neighborhood. The histogram has 64 bins and its range is computed from the global minimum and maximum intensity values in the whole image. The size of the neighborhood is determined from the controls in the lower part of the dialogue.

Kurtosis of Intensity

Kurtosis of the intensity distribution inside the object, also known as the fourth standardized moment. This feature measures the heaviness of the tails for the distribution of intensity over the object's pixels. For multi-channel data, this feature is computed channel-wise. If all pixels in an object have the same value, you may encounter a 'bad features' warning when computing Kurtosis. Kurtosis will have a value of 0 for these objects.

Kurtosis of Intensity in neighborhood

Kurtosis of the intensity distribution in the object neighborhood, also known as the fourth standardized moment. This feature measures the heaviness of the tails for the distribution of intensity over the object's pixels. For multi-channel data, this feature is computed channel-wise. If all pixels in an object have the same value, you may encounter a 'bad features' warning when computing Kurtosis. Kurtosis will have a value of 0 for these objects. The size of the neighborhood is determined from the controls in the lower part of the dialogue.

Maximum intensity

Maximum intensity value inside the object. For multi-channel data, this feature is computed channel-wise.

Maximum intensity in neighborhood

Maximum intensity value in the object neighborhood. For multi-channel data, this feature is computed channel-wise. The size of the neighborhood is determined from the controls in the lower part of the dialogue.

Mean Intensity

Mean intensity inside the object. For multi-channel data, this feature is computed channel-wise.

Mean Intensity in neighborhood

Mean intensity in the object neighborhood. For multi-channel data, this feature is computed channel-wise. The size of the neighborhood is determined from the controls in the lower part of the dialogue.

Minimum intensity

Minimum intensity value inside the object. For multi-channel data, this feature is computed channel-wise.

Minimum intensity in neighborhood

Minimum intensity value in the object neighborhood. For multi-channel data, this feature is computed channel-wise. The size of the neighborhood is determined from the controls in the lower part of the dialogue.

PrincipalAxes

PrincipalAxes, stay tuned for more details

Quantiles of Intensity

Quantiles of the intensity distribution inside the object, in the following order: 0%, 10%, 25%, 50%, 75%, 90%, 100%.

Principal components of the object

Eigenvectors of the PCA on the coordinates of the object's pixels. Very roughly, this corresponds to the axes of an ellipse fit to the object. The axes are ordered starting from the one with the largest eigenvalue.

Center of the object

Average of the coordinates of this object's pixels.

Radii of the object

Eigenvalues of the PCA on the coordinates of the object's pixels. Very roughly, this corresponds to the radii of an ellipse fit to the object. The radii are ordered, with the largest value as first.

Skewness of Intensity

Skewness of the intensity distribution inside the object, also known as the third standardized moment. This feature measures the asymmetry of the intensity distribution inside the object. For multi-channel data, this feature is computed channel-wise. If all pixels in an object have the same value, you may encounter a 'bad features' warning when computing Skewness. Skewness will have a value of 0 for these objects.

Skewness of Intensity in neighborhood

Skewness of the intensity distribution in the object neighborhood, also known as the third standardized moment. This feature measures the asymmetry of the intensity distribution in the object neighborhood. For multi-channel data, this feature is computed channel-wise. If all pixels in an object have the same value, you may encounter a 'bad features' warning when computing Skewness. Skewness will have a value of 0 for these objects. The size of the neighborhood is determined from the controls in the lower part of the dialogue.

Total Intensity

Sum of intensity values for all the pixels inside the object. For multi-channel images, computed channel-wise.

Total Intensity in neighborhood

Sum of intensity values for all the pixels in the object neighborhood. For multi-channel images, computed channel-wise. The size of the neighborhood is determined from the controls in the lower part of the dialogue.

Variance of Intensity

Variance of the intensity distribution inside the object. For multi-channel data, this feature is computed channel-wise.

Variance of Intensity in neighborhood

Variance of the intensity distribution in the object neighborhood. For multi-channel data, this feature is computed channel-wise. The size of the neighborhood is determined from the controls in the lower part of the dialogue.

Convexity

The ratio between the areas of the object and its convex hull (<= 1)

Defect Center

Combined centroid of convexity defects, which are defined as areas of the convex hull, not covered by the original object.

Number of Defects

Total number of defects, i.e. number of connected components in the area of the convex hull, not covered by the original object

Mean Defect Displacement

Mean distance between the centroids of the original object and the centroids of the defects, weighted by defect area.

Kurtosis of Defect Area

Kurtosis (4th standardized moment, measure of tails' heaviness) of the distribution of the areas of convexity defects. Defects are defined as connected components in the area of the convex hull, not covered by the original object.

Mean Defect Area

Average of the areas of convexity defects. Defects are defined as connected components in the area of the convex hull, not covered by the original object.

Skewness of Defect Area

Skewness (3rd standardized moment, measure of asymmetry) of the distribution of the areas of convexity defects. Defects are defined as connected components in the area of the convex hull, not covered by the original object.

Variance of Defect Area

Variance of the distribution of areas of convexity defects. Defects are defined as connected components in the area of the convex hull, not covered by the original object.

Convex Hull Center

Centroid of the convex hull of this object. The axes order is x, y, z

Convex Hull Area

Area of the convex hull of this object

Object Center

Centroid of this object. The axes order is x, y, z

Object Area

Area of this object, computed from the interpixel contour (can be slightly larger than simple size of the object in pixels). This feature is used to compute convexity.

Average Branch Length

Average length of a branch in the skeleton

Number of Branches

Total number of branches in the skeleton of this object.

Diameter

The longest path between two endpoints on the skeleton.

Euclidean Diameter

The Euclidean distance between the endpoints (terminals) of the longest path on the skeleton

Number of Holes

The number of cycles in the skeleton (i.e. the number of cavities in the region)

Center of the Skeleton

The coordinates of the midpoint on the longest path between the endpoints of the skeleton.

Length of the Skeleton

Total length of the skeleton in pixels