Features

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(Intrinsic and Associative Features in FARSIGHT)
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[[Image:Association.png | center| 600px| Illustrating the notion of an associative measurement linking two objects. The list of associative measurements is dependent upon the nature of the two objects and the nature of the relationship between them that we are interested in quantifying. Graph theory offers a natural mathematical representation for associations.]]
 
[[Image:Association.png | center| 600px| Illustrating the notion of an associative measurement linking two objects. The list of associative measurements is dependent upon the nature of the two objects and the nature of the relationship between them that we are interested in quantifying. Graph theory offers a natural mathematical representation for associations.]]
  
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.  
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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.
 
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== Intrinsic Features for Blobs ==
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These features can be calculated with two input images (Data Image and Label Image).
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They are most commonly used for blob-like regions, such as cell nuclei.
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===''Volume''===
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Number of voxels in the object. (Dirk)
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===''Integrated Intensity''===
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Sum of the intensities of all voxels in the object. (Dirk)
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===''Centroid (unweighted and weighted)''===
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The unweighted centroid calculates the center of the object.
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The weighted centroid uses the image intensity values to calculate the intensity center of the object. (Dirk)
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===''Axes Lengths''===
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The length of the axes of the ND hyper-ellipsoid fit to the object. (Dirk)
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===''Eccentricity''===
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Ratio of the distance between the foci of the best-fit hyper-ellipsoid to the length of its major axis. (2D) (Dirk)
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===''Elongation''===
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Ratio of the major axis length to minor axis length of the best-fit hyper-ellipsoid. (2D) (Dirk)
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===''Orientation''===
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Angle between the major axis of the best-fit hyper-ellipsoid and origin. (2D) (Dirk)
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===''Bounding Box Volume''===
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Number of voxels in the bounding box of the object. (Dirk)
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===''Oriented Bounding Box Volume''===
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Number of voxels in the oriented bounding box of the object.
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The oriented bounding box is defined as the bounding box aligned along the axes of the object. (Dirk)
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===''Sum''===
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Same as integrated intensity (ITK statistics)
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===''Mean''===
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Average intensity of voxels in the object (ITK statistics)
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===''Median''===
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Middle intensity of voxels in the object (ITK statistics)
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===''Minimum''===
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Minimum intensity of voxels in the object (ITK statistics)
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===''Maximum''===
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Maximum intensity of voxels in the object (ITK statistics)
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===''Sigma''===
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Standard deviation of intensity of voxels in the object (ITK statistics)
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===''Variance''===
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Variance of intensity of voxels in the object (ITK statistics)
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===''Radius Variation''===
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Standard deviation of distance from surface voxels to centroid (Isaac)
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===''Skew''===
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Skew of the normalized intensity histogram (Isaac) (See Supplement B2)
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===''Energy''===
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Energy of the normalized intensity histogram (Isaac) (See Supplement B2)
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===''Entropy''===
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Entropy of the normalized intensity histogram (Isaac) (See Supplement B2)
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===''Surface Gradient''===
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Average of surface gradients (Isaac)
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===''Interior Gradient''===
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Average of interior gradients (Isaac)
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===''Interior Intensity''===
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Average of interior intensities (Isaac)
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===''Surface Intensity''===
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Average of surface intensities (Isaac)
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===''Intensity Ratio''===
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Ratio of surface intensity to interior intensity (Isaac)
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===''Shared Boundary''===
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Ratio of surface area that touches another object to total surface area (Isaac)
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===''Surface Area''===
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Number of voxels on surface of the object (Isaac)
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===''Shape''===
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Ratio of surface voxels to total voxels - compactness or thinness of object (Isaac) (See Supplement B2)
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==External Links==
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[http://www.insight-journal.org/browse/publication/301 itkLabelGeometryImageFilter]
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[http://kitware.com/products/archive/kitware_quarterly0109.pdf Kitware Source Newsletter]
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Revision as of 13:00, 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 Link
Blobs location, diameter, volume, shape factor, surface area, eccentricity, texture Intrinsic Features of Blobs
Tubes centerline, surface locations, local diameter, branch/crossover points, orientation
Shells thickness, shape factors, brightness
Plate/Laminae thickness, surface area, brightness
Man-made objects location, pose
Foci location, brightness, diameter
Cloud Brightness, texture

The goal of FARSIGHT is to have a sufficiently rich, and well-documented library of intrinsic features available to the user for each morphological category. Another goal is to incorporate appropriate corrections when possible. For instance, most intensity/brightness measurements require a background correction step. Careful documentation of each feature, and clarifying its properties such as the units and invariance are an important goal.

2. Associative Features: Associative features quantify associations/relationships between objects. They can be computed once objects have been delineated, and assigned unique identifiers (IDs).

Illustrating the notion of an associative measurement linking two objects. The list of associative measurements is dependent upon the nature of the two objects and the nature of the relationship between them that we are interested in quantifying. Graph theory offers a natural mathematical representation for associations.

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.

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