temporally_related_to
Related in time by preceding, co-occuring with, or following
follows
precedes
has_property
has_pacs_study
applied_to
To make use of something vs an entity
computed_using
Refers to the process / tool used to produce the computed entity
converted_to
Transformed into something
contains
to have within
defined_by
Which specifies particular properties
delineation_of
Obtained by delineate a contour
has_version_status
developed_by
Created an entity (e.g. a software)
develops
Act to create something new
direction_of
extracted_from
has_basis_function
Specifies basis function of a filter
has_calculation_run
Related to a computational process (calculation run)
has_computed
Has produced as result a computation
has_delineation
has development_status
Specifies what is the status of the development of the SW from first developed test versions to live (in a production environment) versions
has_direction
Specifies the direction / orientation of a filter
has_distance
Specifies the distance (e.g. norm)
has_filter
Specifies the imaging filter
has_function
Speficies the mathematical function
has_GreyLevelRound
Specifies the grey level round
has_intensity_fraction
Specifies the intensity fraction (refers to class IntensityFraction) used to compute the volume at intensity fraction
has_label
has_license
The relationship between an entity and the set of legal restrictions, i.e. license, which are applied in using or otherwise interacting with that entity. Eg. relationship between software and a software license
has_max
Specifies maximum value
has_method
Specifies computational method
has_min
Specifies minimum value
has_orientation
Specifies orientation along the space
has_PartialVolumeCutOff
Specifies Partial Volume cut off value
has_processing
Specifies any post processing applied to an image
has_programming_language
This predicate is used to describe which is the programming language in which a SW or application was written
has_radiomics_feature
Specifies the radiomic feature related to the entity
has_scale
Speficies if a particular scale (e.g. logaritmic has been applied)
has_segmentation_method
This predicates is used to link a Region of Interest with the method used to generate it. It can be both a manual delineation or a SW based the delineation
has_unit
Specifies the unity associated to a quantity
has_version
This predicates is use to link the software with the name of the version. Usually the version name is represented by a number, which represents the release of the SW
has_volume_fraction
The predicate is used to specify the volume fraction (refers to class VolumeFraction) used to compute the intensity at volume fraction (refers to class IntensityAtVolumeFraction) feature
has_voxel_dimensionX
has_voxel_dimensionY
has_voxel_dimensionZ
implemented_in
To put into effect according to or by means of a definite procedure
is_connected_to
is_label_of
is_part_of
is_processing_of
is_programming_language_of
has_featurespace
define feature space
has_imagespace
define image space
is_radiomics_feature_of
performs
is_version_of
performed_by
has_FixedBinSize
originated_from
Created from a certain context
segmented
has_interpolation_method
has_equalisation
has_range
has_FixedBinNumber
has_outlier_removal
is_FixedBinSize_of
is_outlier_removal_of
run_on
http://purl.bioontology.org/ontology/NCIT
GrossTumourVolume
The Gross Tumor Volume (GTV) is the gross demonstrable extent and location of the tumor. The GTV may consist of a primary tumor (primary tumor GTV or GTV-T), metastatic regional node(s) (nodal GTV or GTV-N), or distant metastasis (metastatic GTV, or GTV-M). Typically, different GTVs are defined for the primary tumor and the regional node(s). But in some particular clinical situations, it might well be that the metastatic node cannot be distinguished from the primary tumor, e.g., a nasopharyngeal undifferentiated carcinoma infiltrating posterolaterally into the retropharyngeal space, including possible infiltrated nodes. In such situations, a single GTV encompassing both the primary tumor and the node(s) may be delineated. (ICRU 83)
For more information: http://dx.doi.org/10.1093/jicru/ndq001
http://purl.bioontology.org/ontology/NCIT
Patient
A person who receives medical attention, care, or treatment, or who is registered with medical professional or institution with the purpose to receive medical care when necessary
http://purl.bioontology.org/ontology/NCIT
Scan
The data or image obtained by gathering information with a sensing device
http://purl.bioontology.org/ontology/NCIT
MedicalImage
Any record of a medical imaging event whether physical or electronic
http://purl.bioontology.org/ontology/NCIT
Person
A human being
Scale
A measurement that uses a mathematical transformation of a physical quantity instead of the quantity itself
http://purl.bioontology.org/ontology/NCIT
ImagingRegionOfInterest
A specific area of interest defined by a sequence of image overlays or a sequence of contours described as a single point (for a point ROI) or more than one point (representing and open or closed polygon)
http://purl.bioontology.org/ontology/STY
Event
A broad type for grouping activities, processes and states
http://purl.bioontology.org/ontology/STY
Entity
A broad type for grouping physical and conceptual entities
http://purl.bioontology.org/ontology/STY
PhysicalObject
An object perceptible to the sense of vision or touch or than can be used by an human
http://purl.bioontology.org/ontology/STY
ConceptualEntity
A broad type for grouping abstract entities or concepts
http://purl.bioontology.org/ontology/STY
QuantitativeConcept
A concept which involves the dimensions, quantity or capacity of something using some unit of measure, or which involves the quantitative comparison of entities
http://purl.bioontology.org/ontology/STY
FunctionalConcept
A concept which is of interest because it pertains to the carrying out of a process or activity
ProgrammingLanguage
A language in which source code is written, intended to executed/run by a software interpreter. Programming languages are ways to write instructions that specify what to do, and sometimes, how to do it
http://purl.bioontology.org/ontology/UO
Unit
A unit of measurement is a standardized quantity of a physical quality
http://purl.bioontology.org/ontology/UO
LengthUnit
A unit which is a standard measure of the distance between two points
http://purl.bioontology.org/ontology/UO
Meter
A length unit which is equal to the length of the path traveled by light in vacuum during a time interval of 1/299 792 458 of a second
http://purl.bioontology.org/ontology/UO
Centimeter
A length unit which is equal to one hundredth of a meter or 10^[-2] m
http://purl.bioontology.org/ontology/UO
Millimeter
A length unit which is equal to one thousandth of a meter or 10^[-3] m
http://purl.bioontology.org/ontology/UO
AreaUnit
A unit which is a standard measure of the amount of a 2-dimensional flat surface
http://purl.bioontology.org/ontology/UO
SquareMeter
An area unit which is equal to an area enclosed by a square with sides each 1 meter long
http://purl.bioontology.org/ontology/UO
SquareCentimeter
An area unit which is equal to one thousand of square meter or 10^[-3] m^[2]
http://purl.bioontology.org/ontology/UO
SquareMillimeter
An area unit which is equal to one millionth of a square meter or 10^[-6] m^[2]
http://purl.bioontology.org/ontology/UO
VolumeUnit
A unit which is a standard measure of the amount of space occupied by any substance, whether solid, liquid, or gas
http://purl.bioontology.org/ontology/UO
CubicMeter
A volume unit which is equal to the volume of a cube with edges one meter in length. One cubic meter equals to 1000 liters
http://purl.bioontology.org/ontology/UO
CubicCentimeter
A volume unit which is equal to one millionth of a cubic meter or 10^[-6] m^[3], or to 1 ml
FrequencyUnit
A unit which is a standard measure of the number of repetitive actions in a particular time
Hertz
A frequency unit which is equal to 1 complete cycle of a recurring phenomenon in 1 second
AngleUnit
A unit which is a standard measure of the figure or space formed by the junction of two lines or planes.
Radian
A plane angle unit which is equal to the angle subtended at the center of a circle by an arc equal in length to the radius of the circle, approximately 57 degrees 17 minutes and 44.6 seconds.
Degree
A plane angle unit which is equal to 1/360 of a full rotation or 1.7453310^[-2] rad.
http://purl.bioontology.org/ontology/SEDI
RTDose
http://purl.bioontology.org/ontology/SEDI
RTPlan
http://purl.bioontology.org/ontology/SEDI
RTStructureSet
http://purl.bioontology.org/ontology/ROO
GTV(ROI)
A region of interest based on a delineation of the Gross Tumor Volume. The label adheres to the standardized naming convention proposed by Santanam et al (http://dx.doi.org/10.1016/j.ijrobp.2011.09.054)
http://purl.bioontology.org/ontology/ROO
PTV(ROI)
A region of interest based on a delineation of the Planning Target Volume. The label adheres to the standardized naming convention proposed by Santanam et al (http://dx.doi.org/10.1016/j.ijrobp.2011.09.054)
http://purl.bioontology.org/ontology/ROO
RadiationOncologyRegionOfInterest
The class of regions of interest used in Radiation Oncology and adhering to the Standardized Naming Conventions in Radiation Oncology
http://purl.bioontology.org/ontology/ROO
TargetVolume(ROI)
A region of interest based on a delineation of a Target Volume
http://purl.bioontology.org/ontology/ROO
OrganAtRisk(ROI)
A region of interest based on a delineation of an Organ-At-Risk (OAR)
http://purl.bioontology.org/ontology/ROO
RadiationOncologyDICOMFunctionalConcept
A concept which is of interest because it is related to DICOM objects used in radiation oncology
http://purl.bioontology.org/ontology/ROO
CTV(ROI)
A region of interest based on a delineation of the Clinical Target Volume. The label adheres to the standardized naming convention proposed by Santanam et al (http://dx.doi.org/10.1016/j.ijrobp.2011.09.054)
http://purl.bioontology.org/ontology/ROO
RadiationOncologyFunctionalConcept
A concept which is of interest because it pertains to the carrying out of radiation oncology.
Examples are target and other irradiated volumes, margins etc
http://purl.bioontology.org/ontology/ROO
PlanningTargetVolume
The Planning Target Volume (PTV) is a geometrical concept introduced for treatment planning and evaluation. It is the recommended tool to shape absorbed-dose distributions to ensure that the prescribed absorbed dose will actually be delivered to all parts of the CTV with a clinically acceptable probability, despite geometrical uncertainties such as organ motion and setup variations. It is also used for absorbed-dose prescription and reporting. It surrounds the representation of the CTV with a margin such that the planned absorbed dose is delivered to the CTV. This margin takes into account both the internal and the setup uncertainties. The setup margin accounts specifically for uncertainties in patient positioning and alignment of the therapeutic beams during the treatment planning, and through all treatment sessions. (ICRU 83)
For more information: http://dx.doi.org/10.1093/jicru/ndq001
http://purl.bioontology.org/ontology/ROO
TargetVolume
A Target Volume is a volume of tissue or of a geometrical concept forming the target for irradiation during radiation oncology
http://purl.bioontology.org/ontology/ROO
OrganAtRisk
The Organ-At-Risk (OAR) or critical normal structures are tissues that if irradiated could suffer significant morbidity and thus might influence the treatment planning and/or the absorbed-dose prescription. (ICRU 83)
For more information: http://dx.doi.org/10.1093/jicru/ndq001
DevelopmentStatus
Development status is an information content entity which indicates the maturity of a sofrware entity within the context of the software life cycle.
Alpha
Alpha is a development status which is applied to software by the developer/publisher during initial development and testing. Software designated alpha is commonly unstable and prone to crashing. It may or may not be released publicly
Beta
Beta is a development status which is generally applied to software by the developer/publisher once the majority of features have been implemented, but when the software may still contain bugs or cause crashes or data loss. Software designated beta is often released publicly, either on a general release or to a specific subset of users called beta testers.
Live
Live is a development status which is applied to software that has been designated as suitable for production environments by the developer/publisher. If a non-free product, software at this stage is available for purchase
Obsolete
Sofware is no longer being supplied by the developers/publishers
Mantained
Software has developers actively maintaining it (fixing bugs)
A7WM
IBSI
GLDZM_LargeDistanceLowGreyLevelEmphasis
This feature emphasises runs in the upper right quadrant of the GLDZM, where large zone distances and low grey levels are located.
ACUI
IBSI
GLCM_Contrast
Contrast assesses grey level variations. It is defines as in https://doi.org/10.5589/m02-004
AE86
IBSI
GLCM_ClusterProminence
The cluster prominence is a measure of asymmetry. When the cluster prominence value is high, the image is less symmetric
AMMC
IBSI
IntensityHistogramMode
Most common discretised grey level present in the histogram
BC2M
IBSI
VolumeAtIntensityFraction
It is the largest volume fraction that has an intensity fraction of at least a certain percentage. See also https://doi.org/10.1016/j.patcog.2008.08.011
BJ5W
IBSI
IntensityHistogramUniformity
The uniformity is a measure of the randomness of the grey levels distirbution histogram
BQWJ
IBSI
Compactness2
Compactness 2 is anothere measure to describe how sphere-like the volume is
BRI8
IBSI
AreaDensityMVEE
The surface density minimum volume enclosing ellipsoid is defined as the ratio between the area and the surface of the eilpsoide as defined in feature VolumeDensityMVEE
BTW3
2DAverage
Averaged over slices and directions
BYLV
IBSI
GLSZM_GreyLevelVariance
This feature estimates the variance in zone counts for the grey levels
C0JK
IBSI
SurfaceArea
The surface area is calculated from the ROI mesh, by summing over the face area surfaces
C3I7
IBSI
IntensityHistogramKurtosis
Kurtosis is a measure of how outlier-prone a distribution is. The kurtosis of the normal distribution is 3. Distributions that are more outlier-prone than the normal distribution have kurtosis greater than 3; distributions that are less outlier-prone have kurtosis less than 3
CAS9
IBSI
NGLDM_DependenceCountEnergy
Defined as second moment in Sun and Wee (1983)
CH89
IBSI
IntensityHistogramVariance
Variance of the intensities
CNV2
IBSI
IntensityAtVolumeFractionDifference
The difference between grey levels at two different fractional volumes
CWYJ
IBSI
IntensityHistogramCoefficientOfVariation
The intensity Histogram Coefficient of Variation measures the dispersion of the histogram
D2ZX
IBSI
IntensityHistogramMeanAbsoluteDeviation
Measure of dispersion from the mean of the histogram
D3YU
IBSI
GLCM_DifferenceVariance
The variance for the diagonal probabilities
DDTU
IBSI
VolumeAtIntensityFractionDifference
The difference between the volume fractions at two different intensity fractions. See also https://doi.org/10.1016/j.patcog.2008.08.011
DG8W
IBSI
GLCM_ClusterTendency
The cluster tendency indicates into how many clusters the gray levels present in the image can be classified
DKNJ
IBSI
GLDZM_SmallDistanceHighGreyLevelEmphasis
This feature emphasises runs in the lower left quadrant of the GLDZM, where small zone distances and high grey levels are located
DNX2
IBSI
NGLDM_DependenceCountVariance
This feature estimates the variance in dependence counts for the different dependence counts possible
E8JP
IBSI
GLCM_InverseVariance
The inverse variance feature measures how the gray tone differences are distributed in pair elements
ECT3
IBSI
Variance
The variance from grey level distribution
EQ3F
IBSI
NGLDM_LowDependenceLowGreyLevelEmphasis
This feature emphasises neighbouring grey level dependence counts in the upper left quad- rant of the NGLDM, where low dependence counts and low grey levels are located
FCBV
IBSI
NGLDM_DependenceCountEntropy
The entropy for the dependence counts
FP8K
IBSI
NGLDM_GreyLevelNonUniformity
This feature assesses the distribution of neighbouring grey level dependence counts over the grey values. The feature value is low when dependence counts are equally distributed along grey levels
G3QZ
IBSI
GLRLM_HighGreyLevelRunEmphasis
The HGLRE measures the distribution of high gray level values. The HGRE is high for the image with highgray level values
GBDU
IBSI
GLDZM_ZoneDistanceEntropy
Entropy for the zone distances
GBPN
IBSI
IntensityAtVolumeFraction
Minimum grey level present in at most a certain percentage of the volume. See also https://doi.org/10.1016/j.patcog.2008.08.011
GD3A
IBSI
GLRLM_ShortRunHighGreyLevelEmphasis
The SRHGLE measures the joint distribution of short runs and high gray level values. The SRHGE is high for the image with many short runs and high gray level values
GPMT
IBSI
IntensityHistogramPercentile10
10 Percentile of the histogram
GU8N
IBSI
GLSZM_ZoneSizeEntropy
Entropy related to the zone sizes
GYBY
IBSI
GLCM_JointMaximum
Probability corresponding to the most common grey level co-occurence in the GCLM
HCUG
Morphological
Morphological features describe geometric aspects of a region of interest (ROI), such as area and volume. Morphological features are based on ROI voxel representations of the volume.
HDEZ
IBSI
NGTDM_Complexity
Complex textures are non-uniform and rapid changes in grey levels are common
HJ9O
IBSI
GLRLM_RunEntropy
HTZT
IBSI
GLRLM_ShortRunLowGreyLevelEmphasis
The SRLGLE measures the joint distribution of short runs and low gray level values. The SRLGE is high for the image with many short runs and lower gray level values
HW1V
IBSI
GLSZM_SmallZoneHighGreyLevelEmphasis
This feature emphasises runs in the lower left quadrant of the GLSZM, where small zone sizes and high grey levels are located
I10
I10
The minimum intensity presents in at most 10% of the volume
I10minusI90
I10minusI90
Difference between the intensity at volume fraction 10 and intensity at volume fraction 90
I90
I90
The minimum intensity presents in at most 90% of the volume
IATH
IBSI
GLDZM_ZoneDistanceNonUniformityNormalised
This is the normalised version of the zone distance non-uniformity feature
IAZD
3DMerging
Merged 3D directions
IB1Z
IBSI
GLCM_InverseDifference
Inverse difference is a measure of homogeneity. Grey level co-occurrences with a large difference in levels are weighed less, thus lowering the total feature score. The feature score is maximal if all grey levels are the same
IC23
IBSI
GLRLM_RunLengthNonUniformityNormalised
This is the normalised version of the run length non-uniformity feature
IMOQ
IBSI
NGLDM_HighDependenceEmphasis
This feature emphasises high neighbouring grey level dependence counts
IPET
NeighbourhoodGreyToneDifferenceMatrix
The neighbourhood grey tone difference matrix (NGTDM) contains the sum of grey level differences of pixels/voxels with discretised grey level i and the average discretised grey level of neigh- bouring pixels/voxels within a distance d. See also https://doi.org/10.1109/21.44046
IPH6
IBSI
Kurtosis
Kurtosis is a measure of how outlier-prone a distribution is. The kurtosis of the normal distribution is 3. Distributions that are more outlier-prone than the normal distribution have kurtosis greater than 3; distributions that are less outlier-prone have kurtosis less than 3
IQYR
IBSI
AreaDensityOMBB
The area density oriented bounding box is the ratio between the area and the surface area of the same bounding box as calculated for the VolumeDensityOMBB
ITBB
3DAverage
Averaged over 3D directions
IVPO
IBSI
GLRLM_LongRunLowGreyLevelEmphasis
This feature emphasises runs in the upper right quadrant of the GLRLM, where long run lengths and low grey levels are located
Institution
Institution
An organization founded for a religious, educational, professional, or social purpose
J17V
IBSI
GLSZM_LargeZoneHighGreyLevelEmphasis
This feature emphasises runs in the lower right quadrant of the GLSZM, where large zone sizes and high grey levels are located
JA6D
IBSI
NGLDM_LowDependenceHighGreyLevelEmphasis
This feature emphasises neighbouring grey level dependence counts in the lower left quadrant of the NGLDM, where low dependence counts and high grey levels are located
JJUI
2.5DAverage
Merged per direction and averaged
JN9H
IBSI
GLCM_SecondMeasureOfInformationCorrelation
JNSA
IBSI
GLSZM_GreyLevelNonUniformity
This feature assesses the distribution of zone counts over the grey values. The feature value is low when zone counts are equally distributed along grey levels
K26C
IBSI
GLDZM_HighGreyLevelZoneEmphasis
The feature emphasises high grey levels
KE2A
IBSI
Skewness
The skewness measures the degree of histogram of gray levels asymmetry around the central value
KLMA
IBSI
CentreOfMassShift
The distance between the ROI volume centroid and the intensity-weigthted ROI volume centroid measures the placement of high and low intensity regions within the volume
KLTH
IBSI
GLDZM_LargeDistanceHighGreyLevelEmphasis
This feature emphasises runs in the lower right quadrant of the GLDZM, where large zone distances and high grey levels are located
KOBO
3D
Calculated over the volume
KRCK
IBSI
SphericalDisproportion
Spherical disproportion is a measure to describe how sphere-like the volume is
L0JK
IBSI
Maximum3DDiameter
The maximum 3D diameter is the distance between the two most distant vertices in the ROI mesh vertices sets
LFYI
GreyLevelCoOccurenceMatrix
The grey level co-occurrence matrix (GLCM) is a matrix that expresses how combinations of discretised grey levels of neighbouring pixels, or voxels in a 3D volume, are distributed along one of the image directions. In a 3 dimensional approach to texture analysis, the direct neighbourhood of a voxel consists of the 26 directly neighbouring voxels. Thus, there are 13 unique direction vectors within a neighbourhood volume for distance
LKGHT75
ImageNonUniformityCorrection
LKM7800
PostAcquisitionProcessing
MB4I
IBSI
GLDZM_LargeDistanceEmphasis
This feature emphasises large distances
ManufacturedObject
ManufacturedObject
A physical object made by human beings
MedianFilter
MedianFilter
The median filter works on a n x n subregion of the image. At each position the center voxel is replaced by the median value
N17B
IBSI
Flatness
The flatness is the ratio of the major and the least axis lengths. The flatness is expressed as an inverse ratio: 1 completely not flat; smaller values express objects which are increasingly flatter
N365
IBSI
MoranIndex
Moran’s I index is an indicator of spatial autocorrelation. See also https://doi.org/10.1093/biomet/37.1-2.17
N72L
IBSI
MedianAbsoluteDeviation
Median absolute deviation is similar in concept to mean absolute deviation, but measures dispersion from the median instead of mean
N8CA
IBSI
The energy is the square sum of all the gray levels associated to an image
Energy
NBZI
IBSI
NGLDM_HighDependenceLowGreyLevelEmphasis
This feature emphasises neighbouring grey level dependence counts in the lower left quadrant of the NGLDM, where high dependence counts and low grey levels are located
NDRX
IBSI
GLCM_InverseDifferenceNormalised
Normalized inverse difference as suggested in https://doi.org/10.5589/m02-004
NFRTWQ8
NoiseReduction
NI2N
IBSI
GLCM_Correlation
The correlation feature shows the linear dependence of gray level values in the cooccurence matrix
NPT7
IBSI
GearyMeasure
Geary’s C measures spatial autocorrelation. See also https://doi.org/10.2307%2F2986645
NQ30
IBSI
NGTDM_Busyness
Textures with large changes in grey levels between neighbouring voxels are called busy
NTRS
IBSI
GLCM_DifferenceEntropy
The entropy for the diagonal probabilities
OAE7
IBSI
NGLDM_HighGreyLevelCountEmphasis
The feature emphasises high grey levels
OEEB
IBSI
GLCM_SumVariance
The variance for the cross-diagonal probabilities
OKJI
IBSI
NGLDM_DependenceCountNonUniformityNormalised
This is a normalised version of the dependence count non-uniformity feature
OVBL
IBSI
GLRLM_GreyLevelNonUniformityNormalised
This is the normalised version of the grey level non-uniformity feature
OZ0C
IBSI
IntensityHistogramPercentile90
90 percentile of the histogram
P30P
IBSI
GLSZM_ZonePercentage
This feature assesses the fraction of the number of realised zones and the maximum num- ber of potential zones. Strongly linear or highly uniform ROI volumes produce a low zone percentage
P6QZ
IBSI
GLCM_SumEntropy
The entropy for the cross-diagonal probabilities
P88C
IntensityVolumeHistogram
The intensity-volume histogram (IVH) of the voxel grey level of the ROI intensity mask describes the relationship between discretised grey level i and the fraction of the volume containing at least grey level $i$, $\nu$. See definition in https://doi.org/10.1016/j.patcog.2008.08.011
P9VJ
IBSI
MinorAxisLength
The minor axis length of the ROI provides a measure of how fare the volume extends along the second largest axis. The minor axis length is twice the semi-axis length, determined using the second largest eigenvalue obtained by PCA on the point of the voxel centers
PBX1
IBSI
VolumeDensityAABB
Volume density is the fraction of the ROI volume and a comparison volume. This feature is also called extent
See https://doi.org/10.1016/j.patcog.2008.08.011
Process
Q3CK
IBSI
Elongation
Elongation is the ratio between the major and minor axis lengths. Elongation is espressed as inversed ratio: 1 means completely not elongated (e.g. a sphere). Smaller values express greater elongation of the ROI volume
Q4LE
IBSI
Mean
The mean grey level from grey level distribution
QCDE
IBSI
NGTDM_Coarseness
Grey level differences in coarse textures are generally small due to large-scale patterns. Summing differences gives an indication of the level of the spatial rate of change in intensity
QCFX
IBSI
Sphericity
Sphericity is a further measure to describe how sphere-like the volume is
QG58
IBSI
Percentile10
The statistical 10th percentile of the grey level distribution
QK93
IBSI
GLDZM_GreyLevelVariance
This feature estimates the variance in zone counts for the grey levels
QWB0
IBSI
GLCM_Autocorrelation
The autocorrelation compares all possible pixel pairs and reporting the likelihood that both will be bright as a function of the distance and direction of separation
R3ER
IBSI
VolumeDensityCH
The volume density convex hull is defined as the ration between the volume and the volume of the ROI mesh convex hull set. This feature is also called solidity. See also https://dx.doi.org/10.1016%2Fj.patcog.2008.08.011
R59B
IBSI
AreaDensityAABB
Area Density considers the ratio of the ROI surface area and the surface area of the axis-aligned bounding box enclosing the ROI mesh vertex set. See also https://doi.org/10.1016/j.radonc.2016.07.007
R5YN
IBSI
GLRLM_GreyLevelNonUniformity
This feature assesses the distribution of runs over the grey values (Galloway, 1975). The feature value is low when runs are equally distributed along grey levels
R8DG
IBSI
GLCM_FirstMeasureOfInformationCorrelation
The IMC1 is related to the entropy of the images and gives information on how a pixel value is correlated to its neighbourod
RDD2
IBSI
AreaDensityAEE
The area density approximate enclosing elipsoide is defined as the ratio between the area and the surface of the elipsoide having as axes the 3 eigenvectors from PCA. The surface of the elipsoide is approximated using infinite series
REK0
NeighbourhoodGreyLevelDependenceMatrix
The neighbouring grey level dependence matrix (NGLDM) captures the coarseness of the overall texture and is rotationally invariant. Defined in https://doi.org/10.1016/0734-189X(83)90032-4
RHQZ
IBSI
IntensityHistogramMinimumGradientIntensity
Minimum intensity along the gradient
RNU0
IBSI
Volume
The volume V is calculated from the ROI mesh as indicated in https://doi.org/10.1109/ICIP.2001.958278
RUVG
IBSI
GLDZM_SmallDistanceLowGreyLevelEmphasis
This feature emphasises runs in the upper left quadrant of the GLDZM, where small zone distances and low grey levels are located
S1RA
IBSI
GLDZM_LowGreyLevelZoneEmphasis
Instead of small zone distances, low grey levels are emphasised
SALO
IBSI
InterquartileRange
The interquartile range (IQR) is defined as P75 - P25, where P75 is the 75th percentile of the image matrix, and P25 is the 25th
SKGS
IBSI
Compactness1
The Compactness defines the deviation of the ROI volume from a perfect sphere. Compactness 1 is a measure of how compact, or sphere-like or sphere like the volume is
SLWD
IBSI
IntensityHistogramQuartileCoefficientOfDispersion
The ratio of the difference between the 75 and 25 percentile and their sum
SODN
IBSI
NGLDM_LowDependenceEmphasis
This feature emphasises low neighbouring grey level dependence counts
SUJT
2DMerging
Merged directions per slice and then averaged
SWZ1
IBSI
VolumeDensityMVEE
The volume density minimum volume enclosing ellipsoid is the ratio between the volume and the minimum volume enclosing ellipsoid, calculated as in https://doi.org/10.1016/j.dam.2007.02.013
SXLW
IBSI
GLRLM_RunLengthVariance
This feature estimates the variance in runs for run lengths
Software
TDIC
IBSI
MajorAxisLength
The major axis length is defined as twice the semi-axis length, dtermined using the largest eigenvalue obtained by principal component analysis (PCA) on the point set of voxel centers
TF7R
DifferenceAverage
IBSI
GLCM_DifferentAverage
TL9H
IBSI
NGLDM_LowGreyLevelCountEmphasis
Instead of low neighbouring grey level dependence counts, low grey levels are emphasised
TLU2
IBSI
IntensityHistogramEntropy
It is the Shannon entropy of the histogram
TP0I
GreyLevelRunLengthMatrix
The Grey level run length matrix (GLRLM) like the grey level co-occurrence matrix, GLRLM also assesses the distribution of discretised grey levels in an image or in a stack of images. However, instead of assessing the combination of levels between neighbouring pixels or voxels, GLRLM assesses grey level run lengths. Run length counts the frequency of consecutive voxels with discretised grey level i along direction delta
TU9B
IBSI
GLCM_JointEntropy
As defined in http://dx.doi.org/10.1109/TSMC.1973.4309314
TimeStamp
TimeStamp
UHIW
Statistical
The statistical features describe how grey levels within the region of interest (ROI) are distributed
UR99
IBSI
GLCM_JointVariance
Also called sum of squares as in http://dx.doi.org/10.1109/TSMC.1973.4309314
V10
V10
The maximum percentage volume with at least (10% of the max intensity)
V10minusV90
V10minusV90
Difference between the volume at intensity fraction 10 and volume at intensity fraction 90
V294
IBSI
GLDZM_ZoneDistanceNonUniformity
This features assesses the distribution of zone counts over the different zone distances. The feature value is low when zone counts are equally distributed along zone distances
V3SW
IBSI
GLRLM_LowGreyLevelRunEmphasis
The LGLRE measures the distribution of low gray level values. The LGRE is high for the image with low gray level values
V90
V90
The maximum percentage volume with at least (90% of the max intensity)
VB3A
IBSI
GLSZM_ZoneSizeNonUniformityNormalised
This is a normalised version of the zone size non-uniformity feature
VFT7
IBSI
GLDZM_GreyLevelNonUniformity
This feature assesses the distribution of zone counts over the grey values. The feature value is low when zone counts are equally distributed along grey levels
VIWW
IBSI
GLDZM_ZonePercentage
This feature assesses the fraction of the number of realised zones and the maximum num- ber of potential zones. Strongly linear or highly uniform ROI volumes produce a low zone percentage.
VJGA
IBSI
LocalIntensityPeak
Local Intensity peak is defined as the mean grey level in a 1 cm3
spherical volume, centered on the voxel with the maximum grey level in the ROI intensity mask. See also https://doi.org/10.2967/jnumed.108.057307
VMDZ
GreyLevelDistanceZoneMatrix
The grey level distance zone matrix (GLDZM) counts the number of groups of connected voxels with a specific discretised grey level value and distance to ROI edge (Thibault et al., 2014). The matrix captures the relation between location and grey level.
VQB3
IBSI
IntensityHistogramMinimumGradient
The minimum gradient of the grey level histogram
VTM2
InterpolationParameters
Interpolation algoriths determine the grey level values in the interpolation grid after interpolation of the original grid. Interpolation is commonly used for texture features, to isotropic voxel sizes to be rotationally invariant
W4KF
IBSI
GLRLM_LongRunEmphasis
The LRE measures the distribution of long runs
W92Y
IBSI
GLRLM_RunLengthNonUniformity
This features assesses the distribution of runs over the run lengths (Galloway, 1975). The feature value is low when runs are equally distributed along run lengths.
WF0Z
IBSI
GLCM_InverseDifferenceMoment
Same as the inverse difference feature, but with lower weigths for elements that are further from the diagonal
WIFQ
IBSI
IntensityHistogramMedian
Median value of the histogram
WR0O
IBSI
IntensityHistogramInterquartileRange
Difference between the 75 and 25 interquartiles of the histogram
WRZB
IBSI
IntensityHistogramRobustMeanAbsoluteDeviation
The intensity histogram robust mean absolute deviation is the mean restricted to grey values closer to the center of the distribution
X6K6
IBSI
IntensityHistogramMean
The mean gray level
XMSY
IBSI
GLSZM_LowGreyLevelZoneEmphasis
Measures the distribution of lower gray-level size zones, with a higher value indicating a greater proportion of lower gray-level values and size zones in the image
Y12H
IBSI
Median
The median intensity value
Y1RO
IBSI
GLSZM_GreyLevelNonUniformityNormalised
This is a normalised version of the grey level non-uniformity feature
YEKZ
IBSI
ApproximateVolume
In clinical practice, volumes are commonly determined by counting voxels. For volumes consisting of a large number of voxels (1000s), the differences between approximate volume and mesh-based volume are usually negligible. However for volumes with a low number of voxels (10s to 100s), approximate volume will overestimate volume compared to mesh-based volume. It is therefore only used as a reference feature, and not in the calculation of other morphological features.
YH51
IBSI
GLSZM_LargeZoneLowGreyLevelEmphasis
This feature emphasises runs in the upper right quadrant of the GLSZM, where large zone sizes and low grey levels are located
Z87G
IBSI
NGLDM_DependenceCountNonUniformity
This features assesses the distribution of neighbouring grey level dependence counts over the different dependence counts. The feature value is low when dependence counts are equally distributed
ZGXS
IBSI
GLCM_SumAverage
The average for the cross-diagonal probabilities
ZH1A
IBSI
VolumeDensityOMBB
The volume density oriented mimum bounding box is the ratio between the volume and the volume of the oriented mimum bounding box. . The oriented minimum bounding box of the ROI mesh vertex
set encloses the vertex set and has the smallest possible volume
ZVCW
IntensityHistogram
These features are based on the intensity histogram, whch is generated by discretising the original set of grey levels into grey level bins
ZW7Z
2.5DMerging
merged over all slices
F91
IBSI
GlobalIntensityPeak
The global intensity peak is the highest peak value from the mean intensity calculated with a neighbourhood for every voxel in the ROI intensity mask
GBI
IBSI
GLDZM_SmallDistanceEmphasis
This feature emphasises small distances
RadiomicsFeature
Radiomics features are particular features which can be extracted from objects within an image (e.g. from a region of interest) and that can potentially present a prognostic value (e.g related to the survival probability of a patient).
SegmentationMethods
This class is a container for all the main algorithms used to segment / generate a ROI
Automated
Segmentation is automated performed without requiring any additional interaction by the user. These tecnqiues usually make use of AI
SupervisedMethods
In the supervised category, we can place mostly Artificial Neural Network (ANN) algorithms. Every supervised methods needs a definition of a training set, but also as input a feature vector
SupportVectorMachine
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In the segmentation case the hyperplane is used to separate between foreground and background pixels
NeuralNetwork
The most famous class of ANN. The network needs to be trained on training data before being used. A list of features use by the net has to be defined
UnsupervisedMethods
In the unsupervised category we can put all the methods which does not require having labelled data because they are cluster based
KMean
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster
CMeansFuzzy
This algorithm works by assigning membership to each data point corresponding to each cluster center on the basis of distance between the cluster center and the data point. More the data is near to the cluster center more is its of distance between the cluster center and the data point
Manual
Manual Segmentation of a ROI, usually performed delineating the contour of the region in each slice
InPolygon
SemiAutomated
Segmentation of a ROI which includes a mimimum interaction by the user. E.g delination of contours or selection of starting seed points or the value of a threshold
EdgeBased
This class includes all the methods for segmentation which are based on marking of discontinuities in gray level, color etc., and often these edges represent boundaries between objects. The methods divide an image on the basis of its boundaries
GradientEdgeBased
This method makes use of the gradient operator (first order derivatives) to detect edges
HoughTransformBased
The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure. This voting procedure is carried out in a parameter space, from which object candidates are obtained as local maxima in a so-called accumulator space that is explicitly constructed by the algorithm for computing the Hough transform
LaplacianEdgeBased
This method makes use of the laplacian operator (second order derivatives) to detect edges
MarrHildrethEdgeBased
The Marr–Hildreth edge detection method operates by convolving the image with the Laplacian of the Gaussian function, or, as a fast approximation by Difference of Gaussians. Then, zero crossings are detected in the filtered result to obtain the edges
ModelBased
This class includes all the methods for segmentation which are based on the fact the structure of organs has a repetitive form of geometry and can be modeled probabilistically for variation of shape and geometry
ActiveShape
The ASM model finds the main variations in the training data using Principal Component Analysis (PCA), which enables the model to automatically recognize if a contour is a possible/good object contour
AppereanceModel
An active appearance model (AAM) is a computer vision algorithm for matching a statistical model of appearance - a combination of shape and texture - to a new image
AtlasModel
The segmentation is performed trying to extract prior knowledge from a reference image often called atlas
DeformableModel
The implicit deformable models, also called implicit active contours or level sets, are designed to handle topological changes naturally
RegionBased
This class includes all the the methods for segmentation which are based on working on pixels / regions with similar intensities in order to try to group similar regions. Starting point of most of algorithms is the manual selection of seed points
RegionGrowing
The region growing looks for group of pixels with similar intensities. It starts with a group of pixels (seeds) belonging to the structure of interest. Neighboring pixels are examined one at a time and added to the growing region, if they are sufficiently similar. The procedure continues until no more pixels can be added
RegionSplitting
It is just opposite to region merging and whole image is continuously split until no further splitting of a region is possible
SplitAndMerge
This is the combination of splits and merges utilizing the advantage of the two methods. This method is based on quad quadrant tree representation of data whereby image segment is split into four quadrants provided the original segment is non-uniform in properties. After this the four neighboring squares are merged depending on the uniformity of the region (segments). This split and merge process is continued until no further split and merge is possible
WatershedAlgorithm
Watershed algoritm it Is a region-based tecnique that utilizes image morphology. It requires the selection of at least one marker (seed point). Once the objects are marked they can be grown using the watershed transformation. It is analogous to the notion of a catchment basin of a heightmap. In short, a drop of water following the gradient of an image flows along a path to finally reach a local minimum
ThresholdingBased
This class includes all the the method for segmentation which are based to appying a certain threshold to the pixel of an image. Threshold can be determined manually or looking at the gray level histo distribution
GlobalThresholding
It is based on the assumption that the image has a bimodal histogram and, therefore, the object can be extracted from the background by a simple operation that compares image values with a threshold value T. The result of the process is a binary image
LocalAdaptiveThresholding
Local thresholding can be applied by: splitting an image into subimages and calculating thresholds for each subimage; examining the image intensities in the neighborhood of each pixel
Textural
The textural features describe patterns or the spatial distribution of voxel intensities
ZeroOrder
Features which are directly derived from imaging properties like for example the SUV (Standard Uptake Volume) for PET
SUVMean
SUVMax
FeatureParameterSpace
The feature parameter space is defined as the space that includes all the possible radiomics computation methods that can be applied to certain radiomics features. Subclasses are: aggregation re-segmentation, interpolation, features specific parameters, discretization
AggregationParameters
Specifies how texture matrix are computed and aggregated
2D
Averaged over slices
DiscretizationParameters
Grey level discretisation or quantisation of the ROI is often required to make calculation of texture features tractable (Yip and Aerts, 2016)
Lloyd-Max
The Lloyd-Max algorithm is an iterative clustering method that seeks to minimise mean squared discretisation errors (Max, 1960; Lloyd, 1982).
FixedBinSize
A new bin is assigned every certain grey levels. The fixed bin size method has the advantage of maintaining a direct relationship with the original intensity scale, which could be useful for functional imaging modalities such as PET.
FixedBinNumber
In the fixed bin number method, grey levels are discretised to a fixed number of bins. The number of bins should always be specified
CubicConvolution
Cubic convolution uses the same procedures as cubic spline (refers CubicSpline), but approximates the solution using a convolution filter.
CubicSpline
Cubic spline interpolation draw upon a larger neighbourhood to evaluate a smooth, continuous third-order polynomial at the points of the output grid.
Linear
Linear interpolation makes use of first order polynomial
NearestNeighbour
Nearest neighbour interpolation assigns grey levels in the output grid to the values of the closest voxels in the input grid.
ReSegmentationParameters
Re-segmentation entails updating the ROI mask R based on certain corresponding voxel intensities. Re-segmentation may be performed to exclude voxels from a previously segmented ROI, and is performed after interpolation.
ReSegmentationRange
Re-segmentation may be performed to remove voxels from the intensity mask that fall outside of a specified range. An example is the exclusion of voxels with Hounsfield Units indicating air and bone tissue in the tumour ROI within CT images, or low activity areas in PET images. This class requires to be specified the min and the max of the range with corresponding units
OutlierRemoval
ROI voxels with outlier intensities may be removed from the intensity mask. One method for defining outliers was suggested by Vallie`res et al. (2015) after Collewet et al. (2004).
FeatureSpecificParameters
Container for parameters and computation methods used to derive features
NoDistanceWeighting
No weigths are applied to the computed distances
Voxel
No Meshing algorithm
Symmetrical
Symmetrical texture matrixes
ManhattanNorm
The distance between two points is the sum of the absolute differences of their Cartesian coordinates.
IsoValue
Iso-value algorithm with isolevels
FunctionDistanceWeighting
Weigthening the distance by using a certain function (e.g. exponential)
EuclideanNorm
EuclideanNorm
The "ordinary" straight-line distance between two points in Euclidean space
ChebyshevNorm
Chebyshev distance (or Tchebychev distance), maximum metric, is a metric defined on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension.
Asymmetrical
Asymmetrical texture matrixes
ImageFilterSpace
The image filter space is a container for all the filters that can be applied during the computation of a feature
AbsoluteFiltering
This filter substitutes each original pixel value with its absolute value
ButterworthFilter
This filter applies a Butterworth digital algorithm to an image. It can be both a low-pass or high-pass filter. The cut-off frequency needs to be specified
ColliageFilter
GaborFilter
This filter applies a Gabor filter with a specified wavelength (in pixels) and orientation (in degrees)
GaussianFilter
This filter applies a gaussian kernel with a pre-defined average value and variance to each pixel of an image It is used to blur image and remove details and noise
LaplacianFilter
Convolutes with a laplacian kernel the original image
MeanFilter
The mean filter works on a n x n subregion of the image. At each position the center pixel is replaced by the mean value
RietsFilter
SavitzkyGolayFilter
This filter is also called also called digital smoothing polynomial filters or least-squares smoothing filter. It is used to "smooth out" a noisy signal whose frequency span (without noise) is large. This filter minimizes the least-squares error in fitting a polynomial to frames of noisy data
WaveletFilter
This filter decomposes an image in wavelets. A wavelet series is a representation of a square-integrable (real- or complex-valued) function by a certain orthonormal series generated by a wavelet
FilterProperties
Specifies propery of an image filter
BasisFunction
Function use to decompose an image
Biorthogonal3.5
Coif1
Frequency
Frequency used in a imaging filter
WaveletDirection
HHH
HHL
HLH
HLL
LHH
LHL
LLL
ImageSpace
The image space contains information about an image
ImageVolume
Volume derived from an image
ROIMask
Binary mask as result of a segmentation process
CalculationRunSpace
Properties related to the process used to perform a calculation (e.g. a SW processing something)
ImageOperations
This class is a container for most common image filters used to pre-process an image before feature extraction
AffineTransformations
This class includes all the filters which apply affine transformations (e.g. rotations) which can be applied to an image
Rotation
This filter rotates an image according to a defined rotation matrix
Translation
This filter rotates an image according to a defined rotation matrix
ImageVolume_GreyLevelRound
MathOperations
This class includes all the filters which apply math operations between two / more different images
Average
This filter takes as input two images and produces an image which has as pixel values the average of the values of the starting images
Subtraction
This filter is mainly used with images presenting similarities between them. The goal is to enhance the differences between two images. Subtraction is obtained setting as new pixel value the difference between the corresponding pixels of the two images
Sum
This filter performs the sum, pixel by pixel, of two different images
PixelOperations
This class includes all the filters which apply basic operations on one / multiple pixels of an image
EdgeEnhancement
This filter detects edges in different orientation and enhances them working on pixels with different orientations
HistoEqualization
This filter applies histogram equalization of an image. The normalized histo of the image is interpreted as the probability density function of the intensity of the image. The filter maps the histo of the input image to a new maximally-flat histo
LocalAreaHistoEqualization
This filter applies the same concept of the Histo Equalization filter, but to small, overlapping local areas of the image
Scaling
This class includes all the filters which are used to resize an image. They can be used both to increase or reduce the dimensions
BellInterpolation
Bell uses a kernel to interpolate the pixels of the input image. The kernel is defined as:
0.75-|x| if x<0.5
0.5 (|x| - 1.5)^2 if 0.5 < x < 1.5
0 otherwise
BiCubicInterpolation
The filter makes use of third degree polynomial function to interpolate two pixels
BiLinearInterpolation
This filter uses the same concepts of Nearest Neighbour scaling excepts with interpolation. Instead of copying the neighbouring pixels (which often results in jaggy image), interpolation technique based on surrounding pixels is used to produce much smoother scaling
BSplineInterpolation
This filter performs interpolation using a B-spline of order n
HermiteInterpolation
This filter uses an interpolant based not only on equation for the function values, but also for the derivatives
LanczosInterpolation
This filter uses a convolution kernel to interpolate the pixels of the input image. The kernel is based on the sampling function (sinc)
MithcellInterpolation
This filter uses a kernel to interpolate the pixels of the input image. The kernel is defined as:
1/6 [ ((12-9B-6C)|x|3 + ((-18+12B+6C)|x|2 + (6-2B)) ]; if |x| < 1;
1/6 [ ((-B-6C)|x|3 + (6B+30C)|x|2 + (-12B-48C)|x|2 + (8B+24C) ]; if 1 ≤ |x| < 2;
0 otherwise
NearestNeighbourInterpolation
This filter is a method for multivariate interpolation in one or more dimensions. The algorithm selects the value of the nearest point and does not consider the values of neighbouring points, yileding a piece-wise constant interpolant
TopologicalOperations
This class includes all the filters which are used to resize an image. They can be used both to increase or reduce the dimensions
Closing
This filter performs morphological closing. The morphological operation includes an a dilation followed by an erosion, using the same structuring element for both operations
Dilation
The filter is used to connect features into an image. The dilation operator takes two pieces of data as input: the image to be dilated; a set of coordinate points known as structuring element
Erosion
This filter is used to disconnect features and remove small ones. The erosion operator takes two pieces of data as inputs: the image to be eroded; a set of coordinate points known as structure element
Opening
This filter performs morphological opening. The morphological opening is composed by an erosion followed by an erosion, using the same structuring element for both operations
TopHatFilter
This filter performs morphological top-hat filtering. It computes the morphological opening and then substracts the results from the original image
MathematicalFunction
In mathematics, a function is a relation between a set of inputs and a set of permissible outputs with the property that each input is related to exactly one output.
ExponentialDecay
Defined as exp(-x)
Gaussian
Defined as exp(-x^2)
Inverse
Defined as 1/x
VoxelDimension
VoxelDimensionX
VoxelDimensionY
VoxelDimensionZ
Percentage
CubicMillimeter
A volume unit which is equal to 10^[-9] m^[3]
HounsfieldUnit
The Hounsfield unit (HU) scale is a linear transformation of the original linear attenuation coefficient measurement into one in which the radiodensity of distilled water at standard pressure and temperature (STP) is defined as zero Hounsfield units (HU), while the radiodensity of air at STP is defined as -1000 HU
Logaritmic
Decimal
Distance
The space separating two objects or points.
Chebyschev
Euclidean
Norm
Fraction
Part of a quantitative concept
IntensityFraction
The intensity fraction "I" for grey level "i" in the range G is:
I = (i-min(G)) / (max(G) - min(G))
VolumeFraction
Percentage of total volume of ROI
Max
Min
Orientation
The orientation, angular position, or attitude of an object such as a line, plane or rigid body is part of the description of how it is placed in the space it is in
Angular
Orientation referred to angular directions
ArcCos
Inverse Cosine
ArcSin
Inverse Sine
ArcTan
Inverse Tangent
CosH
Hyperbolic Cosine
Cosine
Cosine Trigonometric Function
Sine
Sine Trigonometric Function
SinH
Hyperbolic Sine
Tangent
Tangent trigonometric Function. Ratio between sine and cosine
TanH
Hyperbolic Tangent
Direction
Specify direction for example for a filter
ROImask_PartialVolumeCutOff
Eulearian
ManufacturedObjectProperties
Properties and characteristics related to any manufactered object
SoftwareProperties
All the properties used to define a SW
License
A software license is a legal instrument (usually by way of contract law, with or without printed material) governing the use or redistribution of software
OpenSource
Open source licenses are licenses that comply with the Open Source Definition — in brief, they allow software to be freely used, modified, and shared
Proprietary
Proprietary software is computer software for which the software's publisher or another person retains intellectual property rights—usually copyright of the source code, but sometimes patent rights
Version
VersionStatus
GLRLMSpecificParameters
MarchingCubes
Haar
MaximumBound
GLCMSpecificParameters
SquareCubicRatio
VolumetricHU
PTVn(ROI)
Equalisation
GSF
IBSI
Minimum
The minimum intensity value
PFV
IBSI
NGLDM_GreyLevelVariance
This feature estimates the variance in dependence counts for the grey levels.
PR8
IBSI
IntensityHistogramMinimum
Minimum gray level bin
QCO
IBSI
GLCM_InverseDifferenceMomentNormalised
Normalized version of the inverse difference moment, as suggested by https://doi.org/10.5589/m02-004
X9X
IBSI
NGTDM_Strength
Feature defined in http://dx.doi.org/10.1371/journal.pone.0093600
RayCasting
IBSI
RobustMeanAbsoluteDeviation
The mean absolute deviation feature may be influenced by outliers. To increase robustness, the set of grey levels can be restricted to those which lie closer to the center of the distribution.
CE
IBSI
IntensityHistogramMaximumGradient
Same feature as defined in https://doi.org/10.1016/j.radonc.2016.07.007
Phantom
IntVolHistSpecificParameters
SquareHU
NGTDMSpecificParameters
DeepLearning
GTVn(ROI)
CTVn(ROI)
MinimumBound
SUV
PartialVolumeEffectCorrection
OJQ
IBSI
IntensityRange
The range of grey levels is the difference between the maximum and the minimum
PR5
IBSI
SurfaceToVolumeRatio
The surface to volume ratio is the ratio between the Surface and the Volume
MorphologicalSpecificParameters
GLDZMSpecificParameters
Pyradiomics
Maximum2DDiameterSlice
Maximum 2D diameter (Slice) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-column (generally the axial) plane.
SquareSUV
Pyradiomics
Maximum2DDiameterRow
Maximum 2D diameter (Row) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the column-slice (usually the sagittal) plane.
Pyradiomics
Maximum2DDiameterColumn
Maximum 2D diameter (Column) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-slice (usually the coronal) plane.
OV
IBSI
GLRLM_ShortRunEmphasis
The SRE measures the distribution of short runs. The SRE is highly dependent on the occurrence of short runs and it gives high value for fine texture the value of SRE is high
Pyradiomics
IntensityHistogramStandardDeviation
The Standard Deviation measures the amount of variation or dispersion from the Mean Value
Pyradiomics
IntensityHistogramTotalEnergy
Total Energy is the value of Energy feature scaled by the volume of the voxel in cubic mm
C7
IBSI
Asphericity
Asphericity describes how much the ROI deviates from a perfect sphere
Pyradiomics
GLCM_Homogeneity1
Deprecated: Same as Inverse Difference
Pyradiomics
GLCM_Homogeneity2
This feature is depracated. Same as Inverse Difference Moment
VolumetricSUV
KUM
IBSI
GLRLM_LongRunHighGreyLevelEmphasis
This feature emphasises runs in the lower right quadrant of the GLRLM, where long run lengths and high grey levels are located
NCY
IBSI
IntensityHistogramMaximum
Highest discretized grey level in the histogram distribution
NSA
IBSI
GLSZM_ZoneSizeVariance
This feature estimates the variance in zone counts for the different zone sizes
FUA
IBSI
MeanAbsoluteDeviation
The mean of the absolute deviations of all voxel intensities around the mean intensitity value
JP3
IBSI
GLSZM_ZoneSizeNonUniformity
This features assesses the distribution of zone counts over the different zone sizes. The feature value is low when zone counts are equally distributed along zone sizes
RNL
IBSI
IntensityHistogramMedianAbsoluteDeviation
Histogram median absolute deviation is conceptually similar to histogram mean absolute deviation, but measures dispersion from the median instead of mean
P8
IBSI
GLSZM_LargeZoneEmphasis
This feature emphasises large zones
GN9
IBSI
GLSZM_HighGreyLevelZoneEmphasis
Measures the distribution of the higher gray-level values, with a higher value indicating a greater proportion of higher gray-level values and size zones in the image
QRC
IBSI
GLSZM_SmallZoneEmphasis
This feature emphasises small zones
RAI
IBSI
GLSZM_SmallZoneLowGreyLevelEmphasis
This feature emphasises runs in the upper left quadrant of the GLSZM, where small zone sizes and low grey levels are located
SPA
IBSI
NGLDM_GreyLevelNonUniformityNormalised
This is the normalised version of the grey level non-uniformity feature
Z3W
IBSI
IntensityHistogramRange
Difference between maximum and minimum of the histogram
ZWQ
IBSI
RootMeanSquare
The square root of the arithmetic mean of the squares of the values
BDE
IBSI
VolumeDensityAEE
The volume density approximate enclosing elipsoide is defined as the ratio between the volume and the volume of the elipsoide having as principal axes the eigenvenctors from the principal component analysis of the ROI
XV8
IBSI
NGLDM_DependenceCountPercentage
This feature assesses the fraction of the number of realised neighbourhoods and the max- imum number of potential neighbourhoods. The feature may be omitted as it evaluates to 1 when complete neighbourhoods are not required, which is the case under our definition
VM
IBSI
GLCM_JointAverage
The grey level weigthed sum of joint probabilities
GR
2.5D
Merged over all slices
HE
IBSI
NGTDM_Contrast
Contrast depends on the dynamic range of the grey levels as well as the spatial frequency of intensity changes
HP3
IBSI
GLDZM_GreyLevelNonUniformityNormalised
This is the normalised version of the grey level non-uniformity feature
J51
IBSI
LeastAxisLength
The least axis is the the axis along which the object is least extended. The least axis is twice the semi-axis length, determined using the smallest eigenvalue obtained by PCA on the point set of voxel centers
NFM
IBSI
GLCM_ClusterShade
The cluster shade is a measure of the skewness of the matrix and it is believed to gauge the perceptual concepts of uniformity. When the cluster age is high the image is asymmetric
T7F
IBSI
AreaDensityCH
The area density convex hull is defined as the ratio between the area and the surface of the convex hull obtained by summing the sum of the area of the faces in the convex hull
TET
IBSI
CoefficientOfVariation
Coefficient of variation is the ratio between the standard deviation and the mean value
WT1
IBSI
GLDZM_ZoneDistanceVariance
This feature estimates the variance in zone counts for the different zone distances
CE5
IBSI
GLRLM_GreyLevelVariance
This feature estimates the variance in runs for the grey levels
DWT
IBSI
Percentile90
The statistical 90th percentile of the grey level distribution
E6O
IBSI
IntensityHistogramMaximumGradientIntensity
The discretized level corresponding to the maximum histogram gradient
S9J
IBSI
GLCM_Dissimilarity
The dissimilarity feature describes the variation of grey levels pair in an image. Dissimilarity always ranges from 0 and 1. It has maximum values when the grey level of the reference and the neighbor pixel is at the extremes of the possibile grey levels in the the texture sample
ZQL
IBSI
GLCM_AngularSecondMoment
It represents the energy of the probability matrix.
Please note that this feature is also called Energy or Uniformity.
IY
IBSI
Maximum
The maximum intensity value
K1
IBSI
IntensityHistogramSkewness
The skewness measures the degree of histogram of gray levels asymmetry around the central value
CMM
IBSI
AreaUnderIVHCurve
Feature as defined in https://doi.org/10.1007/s00259-011-1845-6
QMG
IBSI
NGLDM_HighDependenceHighGreyLevelEmphasis
The high dependence high grey level emphasis feature emphasises neighbouring grey level dependence counts in the lower right quadrant of the NGLDM, where high dependence counts and high grey levels are located
S40
IBSI
QuartileCoefficientOfDispersion
The quartile coefficient of dispersion is defined as the ratio between the difference of the 75 and 25 percentile divided by the sume of 75 and 25 percentile
SAK
GreyLevelSizeZoneMatrix
The grey level size zone matrix (GLSZM) counts the number of groups of connected voxels with a specific discretised grey level value and size as in https://doi.org/10.1109/TBME.2013.2284600. Voxels are con- nected if the neighbouring voxel has the same discretised grey level value
ST6
LocalIntensity
Local intensity features consider voxel intensities within a defined neighbourhood around a center voxel
ZK5
IBSI
GLRLM_RunPercentage
This feature assesses the fraction of the number of realised runs and the maximum number of potential runs (Galloway, 1975). Strongly linear or highly uniform ROI volumes produce a low run percentage
DependenceCoarseness
NGLDMSpecificParameters
N0
IBSI
IntegratedIntensity
Integrated intensity is the average grey level multiplied by the volume. In the context of
18F-FDG-PET, this feature is called total legion glycolysis. See also https://doi.org/10.1016/j.radonc.2011.10.014
CubicMeter
CubicMilliMeter
HU
Live
Mantained
Matlab
Meter
MilliMeter
OpenSource
Proprietary
Python
SquareCentimeter
SquareMeter
SquareMilliMeter
Alpha
Beta
C
C++
CentiMeter
CubicCentiMeter
VolumetricSUV
SquareCubicRatio
SquareHU
SUV
VolumetricHU
SquareSUV