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| | They are most commonly used for blob-like regions, such as nuclei. | | They are most commonly used for blob-like regions, such as nuclei. |
| | | | |
| − | ===''Volume''=== | + | ===Glossary of Notation=== |
| − | 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)
| + | |
| − | ===''Shared Boundary''===
| + | |
| − | 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)
| + | |
| | | | |
| − | ==External Links== | + | <math>p=(x,y,z)</math>: the coordinate of a voxel (three-dimensional point in a volume image) |
| − | [http://www.insight-journal.org/browse/publication/301 itkLabelGeometryImageFilter]
| + | |
| | + | |
| | + | <math>N_{p}</math>: a neighbor voxel of <math>p</math> |
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
2. Associative Features
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 nuclei.
