http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C112913
http://purl.bioontology.org/ontology/NCIT
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C112913
GrossTumourVolume
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C112913
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://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C16960
http://purl.bioontology.org/ontology/NCIT
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C16960
Patient
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C16960
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://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C17999
http://purl.bioontology.org/ontology/NCIT
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C17999
Scan
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C17999
The data or image obtained by gathering information with a sensing device
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C19477
http://purl.bioontology.org/ontology/NCIT
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C19477
MedicalImage
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C19477
Any record of a medical imaging event whether physical or electronic
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C25190
http://purl.bioontology.org/ontology/NCIT
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C25190
Person
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C25190
A human being
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C25664
Scale
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C25664
A measurement that uses a mathematical transformation of a physical quantity instead of the quantity itself
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C85402
http://purl.bioontology.org/ontology/NCIT
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C85402
ImagingRegionOfInterest
http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#C85402
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/T051
http://purl.bioontology.org/ontology/STY
http://purl.bioontology.org/ontology/STY/T051
Event
http://purl.bioontology.org/ontology/STY/T051
A broad type for grouping activities, processes and states
http://purl.bioontology.org/ontology/STY/T071
http://purl.bioontology.org/ontology/STY
http://purl.bioontology.org/ontology/STY/T071
Entity
http://purl.bioontology.org/ontology/STY/T071
A broad type for grouping physical and conceptual entities
http://purl.bioontology.org/ontology/STY/T072
http://purl.bioontology.org/ontology/STY
http://purl.bioontology.org/ontology/STY/T072
PhysicalObject
http://purl.bioontology.org/ontology/STY/T072
An object perceptible to the sense of vision or touch or than can be used by an human
http://purl.bioontology.org/ontology/STY/T077
http://purl.bioontology.org/ontology/STY
http://purl.bioontology.org/ontology/STY/T077
ConceptualEntity
http://purl.bioontology.org/ontology/STY/T077
A broad type for grouping abstract entities or concepts
http://purl.bioontology.org/ontology/STY/T081
http://purl.bioontology.org/ontology/STY
http://purl.bioontology.org/ontology/STY/T081
QuantitativeConcept
http://purl.bioontology.org/ontology/STY/T081
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/T169
http://purl.bioontology.org/ontology/STY
http://purl.bioontology.org/ontology/STY/T169
FunctionalConcept
http://purl.bioontology.org/ontology/STY/T169
A concept which is of interest because it pertains to the carrying out of a process or activity
http://purl.obolibrary.org/obo/IAO_0000025
ProgrammingLanguage
http://purl.obolibrary.org/obo/IAO_0000025
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.obolibrary.org/obo/UO_0000000
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000000
Unit
http://purl.obolibrary.org/obo/UO_0000000
A unit of measurement is a standardized quantity of a physical quality
http://purl.obolibrary.org/obo/UO_0000001
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000001
LengthUnit
http://purl.obolibrary.org/obo/UO_0000001
A unit which is a standard measure of the distance between two points
http://purl.obolibrary.org/obo/UO_0000008
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000008
Meter
http://purl.obolibrary.org/obo/UO_0000008
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.obolibrary.org/obo/UO_0000015
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000015
Centimeter
http://purl.obolibrary.org/obo/UO_0000015
A length unit which is equal to one hundredth of a meter or 10^[-2] m
http://purl.obolibrary.org/obo/UO_0000016
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000016
Millimeter
http://purl.obolibrary.org/obo/UO_0000016
A length unit which is equal to one thousandth of a meter or 10^[-3] m
http://purl.obolibrary.org/obo/UO_0000047
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000047
AreaUnit
http://purl.obolibrary.org/obo/UO_0000047
A unit which is a standard measure of the amount of a 2-dimensional flat surface
http://purl.obolibrary.org/obo/UO_0000080
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000080
SquareMeter
http://purl.obolibrary.org/obo/UO_0000080
An area unit which is equal to an area enclosed by a square with sides each 1 meter long
http://purl.obolibrary.org/obo/UO_0000081
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000081
SquareCentimeter
http://purl.obolibrary.org/obo/UO_0000081
An area unit which is equal to one thousand of square meter or 10^[-3] m^[2]
http://purl.obolibrary.org/obo/UO_0000082
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000082
SquareMillimeter
http://purl.obolibrary.org/obo/UO_0000082
An area unit which is equal to one millionth of a square meter or 10^[-6] m^[2]
http://purl.obolibrary.org/obo/UO_0000095
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000095
VolumeUnit
http://purl.obolibrary.org/obo/UO_0000095
A unit which is a standard measure of the amount of space occupied by any substance, whether solid, liquid, or gas
http://purl.obolibrary.org/obo/UO_0000096
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000096
CubicMeter
http://purl.obolibrary.org/obo/UO_0000096
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.obolibrary.org/obo/UO_0000097
http://purl.bioontology.org/ontology/UO
http://purl.obolibrary.org/obo/UO_0000097
CubicCentimeter
http://purl.obolibrary.org/obo/UO_0000097
A volume unit which is equal to one millionth of a cubic meter or 10^[-6] m^[3], or to 1 ml
http://purl.obolibrary.org/obo/UO_0000105
FrequencyUnit
http://purl.obolibrary.org/obo/UO_0000105
A unit which is a standard measure of the number of repetitive actions in a particular time
http://purl.obolibrary.org/obo/UO_0000106
Hertz
http://purl.obolibrary.org/obo/UO_0000106
A frequency unit which is equal to 1 complete cycle of a recurring phenomenon in 1 second
http://purl.obolibrary.org/obo/UO_0000121
AngleUnit
http://purl.obolibrary.org/obo/UO_0000121
A unit which is a standard measure of the figure or space formed by the junction of two lines or planes.
http://purl.obolibrary.org/obo/UO_0000123
Radian
http://purl.obolibrary.org/obo/UO_0000123
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.
http://purl.obolibrary.org/obo/UO_0000185
Degree
http://purl.obolibrary.org/obo/UO_0000185
A plane angle unit which is equal to 1/360 of a full rotation or 1.7453310^[-2] rad.
http://semantic-dicom.org/dcm#RTDose
http://purl.bioontology.org/ontology/SEDI
http://semantic-dicom.org/dcm#RTDose
RTDose
http://semantic-dicom.org/dcm#RTPlan
http://purl.bioontology.org/ontology/SEDI
http://semantic-dicom.org/dcm#RTPlan
RTPlan
http://semantic-dicom.org/dcm#RTStructureSet
http://purl.bioontology.org/ontology/SEDI
http://semantic-dicom.org/dcm#RTStructureSet
RTStructureSet
http://www.cancerdata.org/roo/100006
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100006
GTV(ROI)
http://www.cancerdata.org/roo/100006
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://www.cancerdata.org/roo/100007
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100007
PTV(ROI)
http://www.cancerdata.org/roo/100007
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://www.cancerdata.org/roo/100031
temporally_related_to
http://www.cancerdata.org/roo/100031
Related in time by preceding, co-occuring with, or following
http://www.cancerdata.org/roo/100032
follows
http://www.cancerdata.org/roo/100034
precedes
http://www.cancerdata.org/roo/100067
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100067
RadiationOncologyRegionOfInterest
http://www.cancerdata.org/roo/100067
The class of regions of interest used in Radiation Oncology and adhering to the Standardized Naming Conventions in Radiation Oncology
http://www.cancerdata.org/roo/100068
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100068
TargetVolume(ROI)
http://www.cancerdata.org/roo/100068
A region of interest based on a delineation of a Target Volume
http://www.cancerdata.org/roo/100069
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100069
OrganAtRisk(ROI)
http://www.cancerdata.org/roo/100069
A region of interest based on a delineation of an Organ-At-Risk (OAR)
http://www.cancerdata.org/roo/100212
has_property
http://www.cancerdata.org/roo/100284
has_pacs_study
http://www.cancerdata.org/roo/100321
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100321
RadiationOncologyDICOMFunctionalConcept
http://www.cancerdata.org/roo/100321
A concept which is of interest because it is related to DICOM objects used in radiation oncology
http://www.cancerdata.org/roo/100590
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100590
CTV(ROI)
http://www.cancerdata.org/roo/100590
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://www.cancerdata.org/roo/100597
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100597
RadiationOncologyFunctionalConcept
http://www.cancerdata.org/roo/100597
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://www.cancerdata.org/roo/100600
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100600
PlanningTargetVolume
http://www.cancerdata.org/roo/100600
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://www.cancerdata.org/roo/100607
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100607
TargetVolume
http://www.cancerdata.org/roo/100607
A Target Volume is a volume of tissue or of a geometrical concept forming the target for irradiation during radiation oncology
http://www.cancerdata.org/roo/100609
http://purl.bioontology.org/ontology/ROO
http://www.cancerdata.org/roo/100609
OrganAtRisk
http://www.cancerdata.org/roo/100609
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
http://www.ebi.ac.uk/swo/maturity/SWO_9000061
DevelopmentStatus
http://www.ebi.ac.uk/swo/maturity/SWO_9000061
Development status is an information content entity which indicates the maturity of a sofrware entity within the context of the software life cycle.
http://www.ebi.ac.uk/swo/maturity/SWO_9000062
Alpha
http://www.ebi.ac.uk/swo/maturity/SWO_9000062
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
http://www.ebi.ac.uk/swo/maturity/SWO_9000063
Beta
http://www.ebi.ac.uk/swo/maturity/SWO_9000063
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.
http://www.ebi.ac.uk/swo/maturity/SWO_9000065
Live
http://www.ebi.ac.uk/swo/maturity/SWO_9000065
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
http://www.ebi.ac.uk/swo/maturity/SWO_9000066
Obsolete
http://www.ebi.ac.uk/swo/maturity/SWO_9000066
Sofware is no longer being supplied by the developers/publishers
http://www.ebi.ac.uk/swo/maturity/SWO_9000073
Mantained
http://www.ebi.ac.uk/swo/maturity/SWO_9000073
Software has developers actively maintaining it (fixing bugs)
A7WM
IBSI
A7WM
GLDZM_LargeDistanceLowGreyLevelEmphasis
A7WM
This feature emphasises runs in the upper right quadrant of the GLDZM, where large zone distances and low grey levels are located.
ACUI
IBSI
ACUI
GLCM_Contrast
ACUI
Contrast assesses grey level variations. It is defines as in https://doi.org/10.5589/m02-004
AE86
IBSI
AE86
GLCM_ClusterProminence
AE86
The cluster prominence is a measure of asymmetry. When the cluster prominence value is high, the image is less symmetric
AMMC
IBSI
AMMC
IntensityHistogramMode
AMMC
Most common discretised grey level present in the histogram
BC2M
IBSI
BC2M
VolumeAtIntensityFraction
BC2M
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
BJ5W
IntensityHistogramUniformity
BJ5W
The uniformity is a measure of the randomness of the grey levels distirbution histogram
BQWJ
IBSI
BQWJ
Compactness2
BQWJ
Compactness 2 is anothere measure to describe how sphere-like the volume is
BRI8
IBSI
BRI8
AreaDensityMVEE
BRI8
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
BYLV
IBSI
BYLV
GLSZM_GreyLevelVariance
BYLV
This feature estimates the variance in zone counts for the grey levels
C0JK
IBSI
C0JK
SurfaceArea
C0JK
The surface area is calculated from the ROI mesh, by summing over the face area surfaces
C3I7
IBSI
C3I7
IntensityHistogramKurtosis
C3I7
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
CAS9
NGLDM_DependenceCountEnergy
CAS9
Defined as second moment in Sun and Wee (1983)
CH89
IBSI
CH89
IntensityHistogramVariance
CH89
Variance of the intensities
CNV2
IBSI
CNV2
IntensityAtVolumeFractionDifference
CNV2
The difference between grey levels at two different fractional volumes
CWYJ
IBSI
CWYJ
IntensityHistogramCoefficientOfVariation
CWYJ
The intensity Histogram Coefficient of Variation measures the dispersion of the histogram
D2ZX
IBSI
D2ZX
IntensityHistogramMeanAbsoluteDeviation
D2ZX
Measure of dispersion from the mean of the histogram
D3YU
IBSI
D3YU
GLCM_DifferenceVariance
D3YU
The variance for the diagonal probabilities
DDTU
IBSI
DDTU
VolumeAtIntensityFractionDifference
DDTU
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
DG8W
GLCM_ClusterTendency
DG8W
The cluster tendency indicates into how many clusters the gray levels present in the image can be classified
DKNJ
IBSI
DKNJ
GLDZM_SmallDistanceHighGreyLevelEmphasis
DKNJ
This feature emphasises runs in the lower left quadrant of the GLDZM, where small zone distances and high grey levels are located
DNX2
IBSI
DNX2
NGLDM_DependenceCountVariance
DNX2
This feature estimates the variance in dependence counts for the different dependence counts possible
E8JP
IBSI
E8JP
GLCM_InverseVariance
E8JP
The inverse variance feature measures how the gray tone differences are distributed in pair elements
ECT3
IBSI
ECT3
Variance
ECT3
The variance from grey level distribution
EQ3F
IBSI
EQ3F
NGLDM_LowDependenceLowGreyLevelEmphasis
EQ3F
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
FCBV
NGLDM_DependenceCountEntropy
FCBV
The entropy for the dependence counts
FP8K
IBSI
FP8K
NGLDM_GreyLevelNonUniformity
FP8K
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
G3QZ
GLRLM_HighGreyLevelRunEmphasis
G3QZ
The HGLRE measures the distribution of high gray level values. The HGRE is high for the image with highgray level values
GBDU
IBSI
GBDU
GLDZM_ZoneDistanceEntropy
GBDU
Entropy for the zone distances
GBPN
IBSI
GBPN
IntensityAtVolumeFraction
GBPN
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
GD3A
GLRLM_ShortRunHighGreyLevelEmphasis
GD3A
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
GPMT
IntensityHistogramPercentile10
GPMT
10 Percentile of the histogram
GU8N
IBSI
GU8N
GLSZM_ZoneSizeEntropy
GU8N
Entropy related to the zone sizes
GYBY
IBSI
GYBY
GLCM_JointMaximum
GYBY
Probability corresponding to the most common grey level co-occurence in the GCLM
HCUG
Morphological
HCUG
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
HDEZ
NGTDM_Complexity
HDEZ
Complex textures are non-uniform and rapid changes in grey levels are common
HJ9O
IBSI
HJ9O
GLRLM_RunEntropy
HTZT
IBSI
HTZT
GLRLM_ShortRunLowGreyLevelEmphasis
HTZT
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
HW1V
GLSZM_SmallZoneHighGreyLevelEmphasis
HW1V
This feature emphasises runs in the lower left quadrant of the GLSZM, where small zone sizes and high grey levels are located
I0010
CubicMeter
I0011
CubicMilliMeter
I0012
HU
I0013
Live
I0016
Mantained
I0017
Matlab
I0019
Meter
I0020
MilliMeter
I0021
OpenSource
I0022
Proprietary
I0023
Python
I0024
SquareCentimeter
I0025
SquareMeter
I0027
SquareMilliMeter
I004
Alpha
I005
Beta
I006
C
I007
C++
I008
CentiMeter
I009
CubicCentiMeter
I10
I10
I10
The minimum intensity presents in at most 10% of the volume
I10minusI90
I10minusI90
I10minusI90
Difference between the intensity at volume fraction 10 and intensity at volume fraction 90
I90
I90
I90
The minimum intensity presents in at most 90% of the volume
IATH
IBSI
IATH
GLDZM_ZoneDistanceNonUniformityNormalised
IATH
This is the normalised version of the zone distance non-uniformity feature
IB1Z
IBSI
IB1Z
GLCM_InverseDifference
IB1Z
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
IC23
GLRLM_RunLengthNonUniformityNormalised
IC23
This is the normalised version of the run length non-uniformity feature
IMOQ
IBSI
IMOQ
NGLDM_HighDependenceEmphasis
IMOQ
This feature emphasises high neighbouring grey level dependence counts
IPET
NeighbourhoodGreyToneDifferenceMatrix
IPET
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
IPH6
Kurtosis
IPH6
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
IQYR
AreaDensityOMBB
IQYR
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
IVPO
IBSI
IVPO
GLRLM_LongRunLowGreyLevelEmphasis
IVPO
This feature emphasises runs in the upper right quadrant of the GLRLM, where long run lengths and low grey levels are located
Institution
Institution
Institution
An organization founded for a religious, educational, professional, or social purpose
J17V
IBSI
J17V
GLSZM_LargeZoneHighGreyLevelEmphasis
J17V
This feature emphasises runs in the lower right quadrant of the GLSZM, where large zone sizes and high grey levels are located
JA6D
IBSI
JA6D
NGLDM_LowDependenceHighGreyLevelEmphasis
JA6D
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
JN9H
IBSI
JN9H
GLCM_SecondMeasureOfInformationCorrelation
JNSA
IBSI
JNSA
GLSZM_GreyLevelNonUniformity
JNSA
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
K26C
GLDZM_HighGreyLevelZoneEmphasis
K26C
The feature emphasises high grey levels
KE2A
IBSI
KE2A
Skewness
KE2A
The skewness measures the degree of histogram of gray levels asymmetry around the central value
KLMA
IBSI
KLMA
CentreOfMassShift
KLMA
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
KLTH
GLDZM_LargeDistanceHighGreyLevelEmphasis
KLTH
This feature emphasises runs in the lower right quadrant of the GLDZM, where large zone distances and high grey levels are located
KRCK
IBSI
KRCK
SphericalDisproportion
KRCK
Spherical disproportion is a measure to describe how sphere-like the volume is
L0JK
IBSI
L0JK
Maximum3DDiameter
L0JK
The maximum 3D diameter is the distance between the two most distant vertices in the ROI mesh vertices sets
LFYI
GreyLevelCoOccurenceMatrix
LFYI
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
MB4I
IBSI
MB4I
GLDZM_LargeDistanceEmphasis
MB4I
This feature emphasises large distances
ManufacturedObject
ManufacturedObject
ManufacturedObject
A physical object made by human beings
MedianFilter
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
N17B
Flatness
N17B
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
N365
MoranIndex
N365
Moran’s I index is an indicator of spatial autocorrelation. See also https://doi.org/10.1093/biomet/37.1-2.17
N72L
IBSI
N72L
MedianAbsoluteDeviation
N72L
Median absolute deviation is similar in concept to mean absolute deviation, but measures dispersion from the median instead of mean
N8CA
IBSI
N8CA
The energy is the square sum of all the gray levels associated to an image
N8CA
Energy
NBZI
IBSI
NBZI
NGLDM_HighDependenceLowGreyLevelEmphasis
NBZI
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
NDRX
GLCM_InverseDifferenceNormalised
NDRX
Normalized inverse difference as suggested in https://doi.org/10.5589/m02-004
NI2N
IBSI
NI2N
GLCM_Correlation
NI2N
The correlation feature shows the linear dependence of gray level values in the cooccurence matrix
NPT7
IBSI
NPT7
GearyMeasure
NPT7
Geary’s C measures spatial autocorrelation. See also https://doi.org/10.2307%2F2986645
NQ30
IBSI
NQ30
NGTDM_Busyness
NQ30
Textures with large changes in grey levels between neighbouring voxels are called busy
NTRS
IBSI
NTRS
GLCM_DifferenceEntropy
NTRS
The entropy for the diagonal probabilities
OAE7
IBSI
OAE7
NGLDM_HighGreyLevelCountEmphasis
OAE7
The feature emphasises high grey levels
OEEB
IBSI
OEEB
GLCM_SumVariance
OEEB
The variance for the cross-diagonal probabilities
OKJI
IBSI
OKJI
NGLDM_DependenceCountNonUniformityNormalised
OKJI
This is a normalised version of the dependence count non-uniformity feature
OVBL
IBSI
OVBL
GLRLM_GreyLevelNonUniformityNormalised
OVBL
This is the normalised version of the grey level non-uniformity feature
OZ0C
IBSI
OZ0C
IntensityHistogramPercentile90
OZ0C
90 percentile of the histogram
P00001
applied_to
P00001
To make use of something vs an entity
P00002
computed_using
P00002
Refers to the process / tool used to produce the computed entity
P00003
converted_to
P00003
Transformed into something
P00006
contains
P00006
to have within
P00009
defined_by
P00009
Which specifies particular properties
P00010
delineation_of
P00010
Obtained by delineate a contour
P000103
has_version_status
P00011
developed_by
P00011
Created an entity (e.g. a software)
P00014
develops
P00014
Act to create something new
P00017
direction_of
P00019
extracted_from
P00019
has_basis_function
P00019
Specifies basis function of a filter
P00020
has_calculation_run
P00020
Related to a computational process (calculation run)
P00021
has_computed
P00021
Has produced as result a computation
P00025
has_delineation
P00028
has development_status
P00028
Specifies what is the status of the development of the SW from first developed test versions to live (in a production environment) versions
P00033
has_direction
P00033
Specifies the direction / orientation of a filter
P00038
has_distance
P00038
Specifies the distance (e.g. norm)
P00039
has_filter
P00039
Specifies the imaging filter
P00040
has_function
P00040
Speficies the mathematical function
P00043
has_GreyLevelRound
P00043
Specifies the grey level round
P00048
has_intensity_fraction
P00048
Specifies the intensity fraction (refers to class IntensityFraction) used to compute the volume at intensity fraction
P00051
has_label
P00055
has_license
P00055
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
P00060
has_max
P00060
Specifies maximum value
P00061
has_method
P00061
Specifies computational method
P00064
has_min
P00064
Specifies minimum value
P00068
has_orientation
P00068
Specifies orientation along the space
P00074
has_PartialVolumeCutOff
P00074
Specifies Partial Volume cut off value
P00080
has_processing
P00080
Specifies any post processing applied to an image
P00082
has_programming_language
P00082
This predicate is used to describe which is the programming language in which a SW or application was written
P00088
has_radiomics_feature
P00088
Specifies the radiomic feature related to the entity
P00089
has_scale
P00089
Speficies if a particular scale (e.g. logaritmic has been applied)
P00092
has_segmentation_method
P00092
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
P00099
has_unit
P00099
Specifies the unity associated to a quantity
P00101
has_version
P00101
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
P00111
has_volume_fraction
P00111
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
P00118
has_voxel_dimensionX
P00123
has_voxel_dimensionY
P00149
has_voxel_dimensionZ
P00156
implemented_in
P00156
To put into effect according to or by means of a definite procedure
P00189
is_connected_to
P00190
is_label_of
P00191
is_part_of
P00192
is_processing_of
P00196
is_programming_language_of
P0394
is_radiomics_feature_of
P0457
performs
P0479
is_version_of
P0546
performed_by
P0876
originated_from
P0876
Created from a certain context
P0901
segmented
P30P
IBSI
P30P
GLSZM_ZonePercentage
P30P
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
P6QZ
GLCM_SumEntropy
P6QZ
The entropy for the cross-diagonal probabilities
P88C
IntensityVolumeHistogram
P88C
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
P9VJ
MinorAxisLength
P9VJ
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
PBX1
VolumeDensityAABB
PBX1
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
Q3CK
IBSI
Q3CK
Elongation
Q3CK
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
Q4LE
Mean
Q4LE
The mean grey level from grey level distribution
QCDE
IBSI
QCDE
NGTDM_Coarseness
QCDE
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
QCFX
Sphericity
QCFX
Sphericity is a further measure to describe how sphere-like the volume is
QG58
IBSI
QG58
Percentile10
QG58
The statistical 10th percentile of the grey level distribution
QK93
IBSI
QK93
GLDZM_GreyLevelVariance
QK93
This feature estimates the variance in zone counts for the grey levels
QWB0
IBSI
QWB0
GLCM_Autocorrelation
QWB0
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
R3ER
VolumeDensityCH
R3ER
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
R59B
AreaDensityAABB
R59B
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
R5YN
GLRLM_GreyLevelNonUniformity
R5YN
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
R8DG
GLCM_FirstMeasureOfInformationCorrelation
R8DG
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
RDD2
AreaDensityAEE
RDD2
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
REK0
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
RHQZ
IntensityHistogramMinimumGradientIntensity
RNU0
IBSI
RNU0
Volume
RNU0
The volume V is calculated from the ROI mesh as indicated in https://doi.org/10.1109/ICIP.2001.958278
RUVG
IBSI
RUVG
GLDZM_SmallDistanceLowGreyLevelEmphasis
RUVG
This feature emphasises runs in the upper left quadrant of the GLDZM, where small zone distances and low grey levels are located
S1RA
IBSI
S1RA
GLDZM_LowGreyLevelZoneEmphasis
S1RA
Instead of small zone distances, low grey levels are emphasised
SALO
IBSI
SALO
InterquartileRange
SALO
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
SKGS
Compactness1
SKGS
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
SLWD
IntensityHistogramQuartileCoefficientOfDispersion
SLWD
It is defined as the ratio of the difference between the 75 and 25 percentile and their sum
SODN
IBSI
SODN
NGLDM_LowDependenceEmphasis
SODN
This feature emphasises low neighbouring grey level dependence counts
SWZ1
IBSI
SWZ1
VolumeDensityMVEE
SWZ1
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
SXLW
GLRLM_RunLengthVariance
SXLW
This feature estimates the variance in runs for run lengths
TDIC
IBSI
TDIC
MajorAxisLength
TDIC
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
TF7R
IBSI
TF7R
GLCM_DifferentAverage
TL9H
IBSI
TL9H
NGLDM_LowGreyLevelCountEmphasis
TL9H
Instead of low neighbouring grey level dependence counts, low grey levels are emphasised
TLU2
IBSI
TLU2
IntensityHistogramEntropy
TLU2
It is the Shannon entropy of the histogram
TP0I
GreyLevelRunLengthMatrix
TP0I
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
TU9B
GLCM_JointEntropy
TU9B
As defined in http://dx.doi.org/10.1109/TSMC.1973.4309314
UHIW
Statistical
UHIW
The statistical features describe how grey levels within the region of interest (ROI) are distributed
UR99
IBSI
UR99
GLCM_JointVariance
UR99
Also called sum of squares as in http://dx.doi.org/10.1109/TSMC.1973.4309314
V10
V10
V10
The maximum percentage volume with at least (10% of the max intensity)
V10minusV90
V10minusV90
V10minusV90
Difference between the volume at intensity fraction 10 and volume at intensity fraction 90
V294
IBSI
V294
GLDZM_ZoneDistanceNonUniformity
V294
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
V3SW
GLRLM_LowGreyLevelRunEmphasis
V3SW
The LGLRE measures the distribution of low gray level values. The LGRE is high for the image with low gray level values
V90
V90
V90
The maximum percentage volume with at least (90% of the max intensity)
VB3A
IBSI
VB3A
GLSZM_ZoneSizeNonUniformityNormalised
VB3A
This is a normalised version of the zone size non-uniformity feature
VFT7
IBSI
VFT7
GLDZM_GreyLevelNonUniformity
VFT7
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
VIWW
GLDZM_ZonePercentage
VIWW
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
VJGA
LocalIntensityPeak
VJGA
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
VMDZ
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
VQB3
IntensityHistogramMinimumGradient
VQB3
The minimum gradient of the grey level histogram
W4KF
IBSI
W4KF
GLRLM_LongRunEmphasis
W4KF
The LRE measures the distribution of long runs
W92Y
IBSI
W92Y
GLRLM_RunLengthNonUniformity
W92Y
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
WF0Z
GLCM_InverseDifferenceMoment
WF0Z
Same as the inverse difference feature, but with lower weigths for elements that are further from the diagonal
WIFQ
IBSI
WIFQ
Median value of the histogram
WIFQ
IntensityHistogramMedian
WR0O
IBSI
WR0O
IntensityHistogramInterquartileRange
WR0O
Difference between the 75 and 25 interquartiles of the histogram
WRZB
IBSI
WRZB
IntensityHistogramRobustMeanAbsoluteDeviation
WRZB
The intensity histogram robust mean absolute deviation is the mean restricted to grey values closer to the center of the distribution
X6K6
IBSI
X6K6
IntensityHistogramMean
X6K6
The mean gray level
XMSY
IBSI
XMSY
GLSZM_LowGreyLevelZoneEmphasis
XMSY
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
Y12H
Median
Y12H
The median intensity value
Y1RO
IBSI
Y1RO
GLSZM_GreyLevelNonUniformityNormalised
Y1RO
This is a normalised version of the grey level non-uniformity feature
YEKZ
IBSI
YEKZ
ApproximateVolume
YEKZ
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
YH51
GLSZM_LargeZoneLowGreyLevelEmphasis
YH51
This feature emphasises runs in the upper right quadrant of the GLSZM, where large zone sizes and low grey levels are located
Z87G
IBSI
Z87G
NGLDM_DependenceCountNonUniformity
Z87G
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
ZGXS
GLCM_SumAverage
ZGXS
The average for the cross-diagonal probabilities
ZH1A
IBSI
ZH1A
VolumeDensityOMBB
ZH1A
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
ZVCW
These features are based on the intensity histogram, whch is generated by discretising the original set of grey levels into grey level bins
0F91
IBSI
0F91
GlobalIntensityPeak
0F91
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
0GBI
IBSI
0GBI
GLDZM_SmallDistanceEmphasis
0GBI
This feature emphasises small distances
000001
RadiomicsFeature
000001
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).
000100
SegmentationMethods
000100
This class is a container for all the main algorithms used to segment / generate a ROI
000110
Automated
000110
Segmentation is automated performed without requiring any additional interaction by the user. These tecnqiues usually make use of AI
000111
SupervisedMethods
000111
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
000112
SupportVectorMachine
000112
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
000113
NeuralNetwork
000113
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
000114
UnsupervisedMethods
000114
In the unsupervised category we can put all the methods which does not require having labelled data because they are cluster based
000115
KMean
000115
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
000116
CMeansFuzzy
000116
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
000120
Manual
000120
Manual Segmentation of a ROI, usually performed delineating the contour of the region in each slice
000121
Poly2Mask
000130
SemiAutomated
000130
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
000131
EdgeBased
000131
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
000132
GradientEdgeBased
000132
This method makes use of the gradient operator (first order derivatives) to detect edges
000133
HoughTransformBased
000133
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
000134
LaplacianEdgeBased
000134
This method makes use of the laplacian operator (second order derivatives) to detect edges
000135
MarrHildrethEdgeBased
000135
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
000136
ModelBased
000136
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
000137
ActiveShape
000137
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
000138
AppereanceModel
000138
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
000139
AtlasModel
000139
The segmentation is performed trying to extract prior knowledge from a reference image often called atlas
000140
DeformableModel
000140
The implicit deformable models, also called implicit active contours or level sets, are designed to handle topological changes naturally
000141
RegionBased
000141
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
000142
RegionGrowing
000142
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
000143
RegionSplitting
000143
000143
It is just opposite to region merging and whole image is continuously split until no further splitting of a region is possible
000144
SplitAndMerge
000144
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
000145
WatershedAlgorithm
000145
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
000146
ThresholdingBased
000146
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
000147
GlobalThresholding
000147
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
000148
LocalAdaptiveThresholding
000148
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
00060
Textural
00060
The textural features describe patterns or the spatial distribution of voxel intensities
00070
ZeroOrder
00070
Features which are directly derived from imaging properties like for example the SUV (Standard Uptake Volume) for PET
00071
SUVMean
00072
SUVMax
001000
FeatureParameterSpace
001000
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
001010
AggregationParameters
001010
Specifies how texture matrix are computed and aggregated
001011
2D
001011
Averaged over slices
001012
3DMerging
001012
Merged 3D directions
001013
3DAverage
001013
Averaged over 3D directions
001014
3D
001014
Calculated over the volume
001015
2DVolumeMerging
001016
2DSliceMerging
001017
2DMerging
001017
Merged directions per slice and then averaged
001018
2DAverage
001018
Averaged over slices and directions
001020
DiscretizationParameters
001020
Grey level discretisation or quantisation of the ROI is often required to make calculation of texture features tractable (Yip and Aerts, 2016)
001021
Lloyd-Max
001021
The Lloyd-Max algorithm is an iterative clustering method that seeks to minimise mean squared discretisation errors (Max, 1960; Lloyd, 1982).
001022
FixedBinSizeEqualization
001023
FixedBinSize
001023
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.
001024
FixedBinNumberEqualization
001025
FixedBinNumber
001025
In the fixed bin number method, grey levels are discretised to a fixed number of bins. The number of bins should always be specified
001030
InterpolationParameters
001030
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
001031
CubicConvolution
001031
Cubic convolution uses the same procedures as cubic spline (refers CubicSpline), but approximates the solution using a convolution filter.
001032
CubicSpline
001032
Cubic spline interpolation draw upon a larger neighbourhood to evaluate a smooth, continuous third-order polynomial at the points of the output grid.
001033
Linear
001033
Linear interpolation makes use of first order polynomial
001034
NearestNeighbour
001034
Nearest neighbour interpolation assigns grey levels in the output grid to the values of the closest voxels in the input grid.
001040
ReSegmentationParameters
001040
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.
001041
ReSegmentationRange
001041
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
001042
OutlierRemoval
001042
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).
001050
FeatureSpecificParameters
001050
Container for parameters and computation methods used to derive features
001051
NoDistanceWeighting
001051
No weigths are applied to the computed distances
001052
Voxel
001052
No Meshing algorithm
001053
Symmetrical
001053
Symmetrical texture matrixes
001054
ManhattanNorm
001054
The distance between two points is the sum of the absolute differences of their Cartesian coordinates.
001055
IsoValue
001055
Iso-value algorithm with isolevels
001056
FunctionDistanceWeighting
001056
Weigthening the distance by using a certain function (e.g. exponential)
001057
EuclideanNorm
001057
The "ordinary" straight-line distance between two points in Euclidean space
001058
ChebyshevNorm
001058
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.
001059
Asymmetrical
001059
Asymmetrical texture matrixes
002000
ImageFilterSpace
002000
The image filter space is a container for all the filters that can be applied during the computation of a feature
002001
AbsoluteFiltering
002001
This filter substitutes each original pixel value with its absolute value
002002
ButterworthFilter
002002
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
002003
ColliageFilter
002004
GaborFilter
002004
This filter applies a Gabor filter with a specified wavelength (in pixels) and orientation (in degrees)
002005
GaussianFilter
002005
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
002006
LaplacianFilter
002006
Convolutes with a laplacian kernel the original image
002007
MeanFilter
002007
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
002008
RietsFilter
002009
SavitzkyGolayFilter
002009
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
002010
WaveletFilter
002010
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
002100
FilterProperties
002100
Specifies propery of an image filter
002101
BasisFunction
002101
Function use to decompose an image
002102
Biorthogonal3.5
002103
Coif1
002104
Frequency
002104
Frequency used in a imaging filter
002105
WaveletDirection
002106
HHH
002107
HHL
002108
HLH
002109
HLL
002110
LHH
002111
LHL
002112
LLL
002200
ImageSpace
002200
The image space contains information about an image
002201
ImageVolume
002201
Volume derived from an image
002202
ROIMask
002202
Binary mask as result of a segmentation process
002300
CalculationRunSpace
002300
Properties related to the process used to perform a calculation (e.g. a SW processing something)
003000
ImageOperations
003000
This class is a container for most common image filters used to pre-process an image before feature extraction
003001
AffineTransformations
003001
This class includes all the filters which apply affine transformations (e.g. rotations) which can be applied to an image
003002
Rotation
003002
This filter rotates an image according to a defined rotation matrix
003003
Translation
003003
This filter rotates an image according to a defined rotation matrix
003004
GreyLevelRound
003005
MathOperations
003005
This class includes all the filters which apply math operations between two / more different images
003006
Average
003006
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
003007
Subtraction
003007
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
003008
Sum
003008
This filter performs the sum, pixel by pixel, of two different images
003009
PixelOperations
003009
This class includes all the filters which apply basic operations on one / multiple pixels of an image
003010
EdgeEnhancement
003010
This filter detects edges in different orientation and enhances them working on pixels with different orientations
003011
HistoEqualization
003011
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
003012
LocalAreaHistoEqualization
003012
This filter applies the same concept of the Histo Equalization filter, but to small, overlapping local areas of the image
003013
Scaling
003013
This class includes all the filters which are used to resize an image. They can be used both to increase or reduce the dimensions
003014
BellInterpolation
003014
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
003015
BiCubicInterpolation
003015
The filter makes use of third degree polynomial function to interpolate two pixels
003016
BiLinearInterpolation
003016
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
003017
BSplineInterpolation
003017
This filter performs interpolation using a B-spline of order n
003018
HermiteInterpolation
003018
This filter uses an interpolant based not only on equation for the function values, but also for the derivatives
003019
LanczosInterpolation
003019
This filter uses a convolution kernel to interpolate the pixels of the input image. The kernel is based on the sampling function (sinc)
003020
MithcellInterpolation
003020
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
003021
NearestNeighbourInterpolation
003021
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
003022
TopologicalOperations
003022
This class includes all the filters which are used to resize an image. They can be used both to increase or reduce the dimensions
003023
Closing
003023
This filter performs morphological closing. The morphological operation includes an a dilation followed by an erosion, using the same structuring element for both operations
003024
Dilation
003024
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
003025
Erosion
003025
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
003026
Opening
003026
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
003027
TopHatFilter
003027
This filter performs morphological top-hat filtering. It computes the morphological opening and then substracts the results from the original image
004000
MathematicalFunction
004000
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.
004001
ExponentialDecay
004001
Defined as exp(-x)
004002
Gaussian
004002
Defined as exp(-x^2)
004003
Inverse
004003
Defined as 1/x
005000
VoxelDimension
005001
VoxelDimensionX
005002
VoxelDimensionY
005003
VoxelDimensionZ
005004
Percentage
005006
CubicMillimeter
005006
A volume unit which is equal to 10^[-9] m^[3]
005007
HounsfieldUnit
005007
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
005009
Logaritmic
005010
Decimal
005011
Distance
005011
The space separating two objects or points.
005012
Chebyschev
005013
Euclidean
005014
Norm
005020
Fraction
005020
Part of a quantitative concept
005021
IntensityFraction
005021
The intensity fraction "I" for grey level "i" in the range G is:
I = (i-min(G)) / (max(G) - min(G))
005022
VolumeFraction
005022
Percentage of total volume of ROI
005023
Max
005024
Min
005025
Orientation
005025
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
005026
Angular
005026
Orientation referred to angular directions
005027
ArcCos
005027
Inverse Cosine
005028
ArcSin
005028
Inverse Sine
005029
ArcTan
005029
Inverse Tangent
005030
CosH
005030
Hyperbolic Cosine
005031
Cosine
005031
Cosine Trigonometric Function
005034
Sine
005034
Sine Trigonometric Function
005035
SinH
005035
Hyperbolic Sine
005036
Tangent
005036
Tangent trigonometric Function. Ratio between sine and cosine
005037
TanH
005037
Hyperbolic Tangent
005040
Direction
005040
Specify direction for example for a filter
005042
PartialVolumeCutOff
005060
Eulearian
006000
ManufacturedObjectProperties
006000
Properties and characteristics related to any manufactered object
006001
SoftwareProperties
006001
All the properties used to define a SW
006008
License
006008
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
006009
OpenSource
006009
Open source licenses are licenses that comply with the Open Source Definition — in brief, they allow software to be freely used, modified, and shared
006010
Proprietary
006010
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
006020
Version
006040
VersionStatus
1GSF
IBSI
1GSF
Minimum
1GSF
The minimum intensity value
1PFV
IBSI
1PFV
NGLDM_GreyLevelVariance
1PFV
This feature estimates the variance in dependence counts for the grey levels.
1PR8
IBSI
1PR8
IntensityHistogramMinimum
1PR8
Minimum gray level bin
1QCO
IBSI
1QCO
GLCM_InverseDifferenceMomentNormalised
1QCO
Normalized version of the inverse difference moment, as suggested by https://doi.org/10.5589/m02-004
1X9X
IBSI
1X9X
NGTDM_Strength
1X9X
Feature defined in http://dx.doi.org/10.1371/journal.pone.0093600
1128
IBSI
1128
RobustMeanAbsoluteDeviation
1128
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.
12CE
IBSI
12CE
IntensityHistogramMaximumGradient
12CE
Same feature as defined in https://doi.org/10.1016/j.radonc.2016.07.007
2OJQ
IBSI
2OJQ
IntensityRange
2OJQ
The range of grey levels is the difference between the maximum and the minimum
2PR5
IBSI
2PR5
SurfaceToVolumeRatio
2PR5
The surface to volume ratio is the ratio between the Surface and the Volume
2130
Pyradiomics
2130
Maximum2DDiameterSlice
2130
Maximum 2D diameter (Slice) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-column (generally the axial) plane.
2140
Pyradiomics
2140
Maximum2DDiameterRow
2140
Maximum 2D diameter (Row) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the column-slice (usually the sagittal) plane.
2150
Pyradiomics
2150
Maximum2DDiameterColumn
2150
Maximum 2D diameter (Column) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-slice (usually the coronal) plane.
22OV
IBSI
22OV
GLRLM_ShortRunEmphasis
22OV
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
24160
Pyradiomics
24160
IntensityHistogramStandardDeviation
24160
The Standard Deviation measures the amount of variation or dispersion from the Mean Value
2490
Pyradiomics
2490
IntensityHistogramTotalEnergy
2490
Total Energy is the value of Energy feature scaled by the volume of the voxel in cubic mm
25C7
IBSI
25C7
Asphericity
25C7
Asphericity describes how much the ROI deviates from a perfect sphere
26200
Pyradiomics
26200
GLCM_Homogeneity1
26200
26200
Deprecated: Same as Inverse Difference
26201
Pyradiomics
26201
GLCM_Homogeneity2
26201
26201
This feature is depracated. Same as Inverse Difference Moment
3KUM
IBSI
3KUM
GLRLM_LongRunHighGreyLevelEmphasis
3KUM
This feature emphasises runs in the lower right quadrant of the GLRLM, where long run lengths and high grey levels are located
3NCY
IBSI
3NCY
IntensityHistogramMaximum
3NCY
Highest discretized grey level in the histogram distribution
3NSA
IBSI
3NSA
GLSZM_ZoneSizeVariance
3NSA
This feature estimates the variance in zone counts for the different zone sizes
4FUA
IBSI
4FUA
MeanAbsoluteDeviation
4FUA
The mean of the absolute deviations of all voxel intensities around the mean intensitity value
4JP3
IBSI
4JP3
GLSZM_ZoneSizeNonUniformity
4JP3
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
4RNL
IBSI
4RNL
IntensityHistogramMedianAbsoluteDeviation
4RNL
Histogram median absolute deviation is conceptually similar to histogram mean absolute deviation, but measures dispersion from the median instead of mean
4RNL
Similar as the intensity histogram median absolute deviation, but considering only grey levels closer to the center of the distribution
48P8
IBSI
48P8
GLSZM_LargeZoneEmphasis
48P8
This feature emphasises large zones
5GN9
IBSI
5GN9
GLSZM_HighGreyLevelZoneEmphasis
5GN9
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
5QRC
IBSI
5QRC
GLSZM_SmallZoneEmphasis
5QRC
This feature emphasises small zones
5RAI
IBSI
5RAI
GLSZM_SmallZoneLowGreyLevelEmphasis
5RAI
This feature emphasises runs in the upper left quadrant of the GLSZM, where small zone sizes and low grey levels are located
5SPA
IBSI
5SPA
NGLDM_GreyLevelNonUniformityNormalised
5SPA
This is the normalised version of the grey level non-uniformity feature
5Z3W
IBSI
5Z3W
IntensityHistogramRange
5Z3W
Difference between maximum and minimum of the histogram
5ZWQ
IBSI
5ZWQ
RootMeanSquare
5ZWQ
The square root of the arithmetic mean of the squares of the values
6BDE
IBSI
6BDE
VolumeDensityAEE
6BDE
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
6XV8
IBSI
6XV8
NGLDM_DependenceCountPercentage
6XV8
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
60VM
IBSI
60VM
GLCM_JointAverage
60VM
The grey level weigthed sum of joint probabilities
65HE
IBSI
65HE
NGTDM_Contrast
65HE
Contrast depends on the dynamic range of the grey levels as well as the spatial frequency of intensity changes
7HP3
IBSI
7HP3
GLDZM_GreyLevelNonUniformityNormalised
7HP3
This is the normalised version of the grey level non-uniformity feature
7J51
IBSI
7J51
LeastAxisLength
7J51
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
7NFM
IBSI
7NFM
GLCM_ClusterShade
7NFM
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
7T7F
IBSI
7T7F
AreaDensityCH
7T7F
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
7TET
IBSI
7TET
CoefficientOfVariation
7TET
Coefficient of variation is the ratio between the standard deviation and the mean value
7WT1
IBSI
7WT1
GLDZM_ZoneDistanceVariance
7WT1
This feature estimates the variance in zone counts for the different zone distances
8CE5
IBSI
8CE5
GLRLM_GreyLevelVariance
8CE5
This feature estimates the variance in runs for the grey levels
8DWT
IBSI
8DWT
Percentile90
8DWT
The statistical 90th percentile of the grey level distribution
8E6O
IBSI
8E6O
IntensityHistogramMaximumGradientIntensity
8E6O
The discretized level corresponding to the maximum histogram gradient
8S9J
IBSI
8S9J
GLCM_Dissimilarity
8S9J
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
8ZQL
IBSI
8ZQL
GLCM_AngularSecondMoment
8ZQL
It represents the energy of the probability matrix.
Please note that this feature is also called Energy or Uniformity.
84IY
IBSI
84IY
Maximum
84IY
The maximum intensity value
88K1
IBSI
88K1
IntensityHistogramSkewness
88K1
The skewness measures the degree of histogram of gray levels asymmetry around the central value
9CMM
IBSI
9CMM
AreaUnderIVHCurve
9CMM
Feature as defined in https://doi.org/10.1007/s00259-011-1845-6
9QMG
IBSI
9QMG
NGLDM_HighDependenceHighGreyLevelEmphasis
9QMG
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
9S40
IBSI
9S40
QuartileCoefficientOfDispersion
9S40
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
9SAK
GreyLevelSizeZoneMatrix
9SAK
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
9ST6
LocalIntensity
9ST6
Local intensity features consider voxel intensities within a defined neighbourhood around a center voxel
9ZK5
IBSI
9ZK5
GLRLM_RunPercentage
9ZK5
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
99N0
IBSI
99N0
IntegratedIntensity
99N0
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