Features
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'''1. Intrinsic Features: ''' Intrinsic features are measurements that quantify aspects of the morphology and/or appearance of objects in a single image channel. They can be computed based on a segmentation (object delineation) that results in a set of pixel/voxel labels. One must pay close attention to the units of the features. Also keep in mind the possibility of non-isotropic images - this is a common occurrence in biological microscopy. Usually, the axial dimension of a voxel is much greater than its lateral dimensions. To obtain feature measurements in physical units, one must scale pixel/voxel based feature values appropriately. The intrinsic measurements of each object are directly determined by its morphological class. Some examples are noted in the table below. | '''1. Intrinsic Features: ''' Intrinsic features are measurements that quantify aspects of the morphology and/or appearance of objects in a single image channel. They can be computed based on a segmentation (object delineation) that results in a set of pixel/voxel labels. One must pay close attention to the units of the features. Also keep in mind the possibility of non-isotropic images - this is a common occurrence in biological microscopy. Usually, the axial dimension of a voxel is much greater than its lateral dimensions. To obtain feature measurements in physical units, one must scale pixel/voxel based feature values appropriately. The intrinsic measurements of each object are directly determined by its morphological class. Some examples are noted in the table below. | ||
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Revision as of 12:30, 28 April 2009
FARSIGHT contains a rich and constantly growing library of software routines to compute diverse measurements from images. Measurements are also commonly referred to as "features" in the image analysis community. Broadly speaking, there are two classes of features in FARSIGHT:
1. Intrinsic Features: Intrinsic features are measurements that quantify aspects of the morphology and/or appearance of objects in a single image channel. They can be computed based on a segmentation (object delineation) that results in a set of pixel/voxel labels. One must pay close attention to the units of the features. Also keep in mind the possibility of non-isotropic images - this is a common occurrence in biological microscopy. Usually, the axial dimension of a voxel is much greater than its lateral dimensions. To obtain feature measurements in physical units, one must scale pixel/voxel based feature values appropriately. The intrinsic measurements of each object are directly determined by its morphological class. Some examples are noted in the table below.
| Object Morphology | Examples of Intrinsic Measurements |
|---|---|
| Blobs | location, diameter, volume, shape factor, surface area, eccentricity |
| Tubes | centerline, surface locations, local diameter, branch points |
| Shells | thickness, shape factors |
| Plate/Laminae | thickness, surface area |
| Man-made objects | location, pose |
| Foci | location, brightness, diameter |
| Cloud | Brightness, texture |
These parameters directly define the intrinsic measurements that are always computed. For example, we are interested in sizes and shapes of blob-like objects, but lengths, widths, and branching patterns of tube-like objects. The goal of FARSIGHT is to have a sufficiently rich library of intrinsic features available to the user to select from.
2. Associative Features: Associative features quantify associations/relationships between objects. They can be computed once objects have been delineated. Some associative features involve objects and image data. Others relate objects to other objects. Associative features are quite diverse, and there are a combinatorially large number of possible associations. However, a much smaller number of them are are interesting and/or useful for a given investigation. The goal of FARSIGHT is to have a versatile set of 'programmable' computational methods available to the user. These are based on the simplest notions such as spatial proximity, neighborhood, and adjacency.
Intrinsic Features for Blobs
These features can be calculated with two input images (Data Image and Label Image). They are most commonly used for blob-like regions, such as cell nuclei.
Volume
Number of voxels in the object. (Dirk)
Integrated Intensity
Sum of the intensities of all voxels in the object. (Dirk)
Centroid (unweighted and weighted)
The unweighted centroid calculates the center of the object. The weighted centroid uses the image intensity values to calculate the intensity center of the object. (Dirk)
Axes Lengths
The length of the axes of the ND hyper-ellipsoid fit to the object. (Dirk)
Eccentricity
Ratio of the distance between the foci of the best-fit hyper-ellipsoid to the length of its major axis. (2D) (Dirk)
Elongation
Ratio of the major axis length to minor axis length of the best-fit hyper-ellipsoid. (2D) (Dirk)
Orientation
Angle between the major axis of the best-fit hyper-ellipsoid and origin. (2D) (Dirk)
Bounding Box Volume
Number of voxels in the bounding box of the object. (Dirk)
Oriented Bounding Box Volume
Number of voxels in the oriented bounding box of the object. The oriented bounding box is defined as the bounding box aligned along the axes of the object. (Dirk)
Sum
Same as integrated intensity (ITK statistics)
Mean
Average intensity of voxels in the object (ITK statistics)
Median
Middle intensity of voxels in the object (ITK statistics)
Minimum
Minimum intensity of voxels in the object (ITK statistics)
Maximum
Maximum intensity of voxels in the object (ITK statistics)
Sigma
Standard deviation of intensity of voxels in the object (ITK statistics)
Variance
Variance of intensity of voxels in the object (ITK statistics)
Radius Variation
Standard deviation of distance from surface voxels to centroid (Isaac)
Skew
Skew of the normalized intensity histogram (Isaac) (See Supplement B2)
Energy
Energy of the normalized intensity histogram (Isaac) (See Supplement B2)
Entropy
Entropy of the normalized intensity histogram (Isaac) (See Supplement B2)
Surface Gradient
Average of surface gradients (Isaac)
Interior Gradient
Average of interior gradients (Isaac)
Interior Intensity
Average of interior intensities (Isaac)
Surface Intensity
Average of surface intensities (Isaac)
Intensity Ratio
Ratio of surface intensity to interior intensity (Isaac)
Ratio of surface area that touches another object to total surface area (Isaac)
Surface Area
Number of voxels on surface of the object (Isaac)
Shape
Ratio of surface voxels to total voxels - compactness or thinness of object (Isaac) (See Supplement B2)
