#OWLClass_010000
Radiomics features are particular features which can be extracted from objects within an image (e.g. from a region of interest) and that ca potentially present a prognostic value (e.g related to the survival probability of a patient)
#OWLClass_010000
RadiomicsFeatures
#OWLClass_010001
The first order statistics features provide information related to the gray level distribution of the image
#OWLClass_010001
FirstOrderStatistics
#OWLClass_010002
The shape and size based features provide information related to the shape of the tumor, including for example its size
#OWLClass_010002
ShapeAndSizeBased
#OWLClass_010003
The textural features describe patterns or the spatial distribution of voxel intensities
#OWLClass_010003
Textural
#OWLClass_010004
These features are based and computed on the GLCM which is a matrix describing the second ordedr joint probability function of an image, where the (i,j) th element represents the number of times the combinations of intensity level I and j occur in two pixels in the image that are separated by a distance of delta pixels in a certain direction alpha
#OWLClass_010004
GrayLevelCoOccurenceMatrixBased
#OWLClass_010005
These features are based and computed on the Run length metrics. Run length metrics quantify gray level runs in an image.A grey level run is defined as the length in number of pixels of consecutive pixels that have the same gray level value
#OWLClass_010005
GrayLevelRunLengthMatrixBased
#OWLClass_010006
The energy is the sum of all the gray levels associated to an image. Since grey levels are usually associated to energy deposited by for example photons, this is a measured of total energy deposited and revealed
#OWLClass_010006
Energy
#OWLClass_010007
Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image
#OWLClass_010007
Entropy
#OWLClass_010008
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
#OWLClass_010008
Kurtosis
#OWLClass_010009
The maximum intensity value of the matrix of the image
#OWLClass_010009
Maximum
#OWLClass_010010
Mean Value for the grey level distribution histogram
#OWLClass_010010
Mean
#OWLClass_010011
The mean of the absolute deviations of all voxel intensities around the mean intensitity value
#OWLClass_010011
MeanAbsoluteDeviation
#OWLClass_010012
The median intensity value of the image matrix
#OWLClass_010012
Median
#OWLClass_010013
The minimum intensity value of the image matrix
#OWLClass_010013
Mimimum
#OWLClass_010014
The range of intensity values of the image matrix
#OWLClass_010014
Range
#OWLClass_010015
The square root of the arithmetic mean of the squares of the values in the image matrix
#OWLClass_010015
RootMeanSquare
#OWLClass_010016
The skewness measures the degree of histogram of gray levels asymmetry around the central value
#OWLClass_010016
Skewness
#OWLClass_010017
The standard deviation is a measure of the histogram dispersion, that is a measure of how much the gray levels differ from the mean
#OWLClass_010017
StandardDeviation
#OWLClass_010018
The uniformity is a measure of the randomness of the gray level distirbution histogram
#OWLClass_010018
Uniformity
#OWLClass_010019
The variance is a measure of the histogram dispersion, that is a measure of how much the gray levels differ from the mean
#OWLClass_010019
Variance
#OWLClass_010020
Compactness 1 is a measure of how compact the shape of the tumor is relative to a sphere (most compact)
#OWLClass_010020
Compactness1
#OWLClass_010021
Compactness 2 is a measure of how compact the shape of the tumor is relative to a sphere (most compact). It is a dimensionless measure, independent of scale and orientation
#OWLClass_010021
Compactness2
#OWLClass_010022
The maximum three-dimensional tumor diameter is measured as the largest pairwise Euclidean distance between voxels on the surface of the tumor volume
#OWLClass_010022
Maximum3DDiameter
#OWLClass_010023
The spherical disproportion describes how much the surface of the volume differentiates from a sphere having the same volume of the tumor
#OWLClass_010023
SphericalDisproportion
#OWLClass_010024
The sphericity describes how much the the tumor is rounded
#OWLClass_010024
Sphericity
#OWLClass_010025
The surface area provides information about the dimension of the tumor. It is calculated by triangulation (i.e. dividing the surface into connected triangles)
#OWLClass_010025
SurfaceArea
#OWLClass_010026
The surface to volume ratio describes how much the shape of the volume is elongated or not
#OWLClass_010026
SurfaceToVolumeRatio
#OWLClass_010027
The volume of the tumor is determined by counting the number of pixels in the tumor region and multiplying this value by the voxel size
#OWLClass_010027
Volume
#OWLClass_010028
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
#OWLClass_010028
Autocorrelation
#OWLClass_010029
The cluster prominence is a measure of asymmetry. When the cluster prominence value is high, the image is less symmetric
#OWLClass_010029
ClusterProminence
#OWLClass_010030
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
#OWLClass_010030
ClusterShade
#OWLClass_010031
The cluster tendency indicates, into how many clusters the gray levels present in the image can be classified
#OWLClass_010031
ClusterTendency
#OWLClass_010032
The contrast is a measure of intensity or gray level varations between the reference pixel and its neighbor. Large contrast reflects large intensity differences
#OWLClass_010032
Contrast
#OWLClass_010033
The correlation feature shows the linear dependence of gray level values in the cooccurence matrix
#OWLClass_010033
Correlation
#OWLClass_010034
The difference entropy is smallest when the probability values P are unequal and largest when the probability values are equal
#OWLClass_010034
DifferenceEntropy
#OWLClass_010035
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
#OWLClass_010035
Dissimilarity
#OWLClass_010036
Sum of all the values in the concurrence matrix
#OWLClass_010036
Energy_Concurrence
#OWLClass_010037
Measure of the spatial disorder in the concurrence matrix
#OWLClass_010037
Entropy_Concurrence
#OWLClass_010038
The homogeneity feature measures the uniformity of the non-zero entries in the concurrence matrix. The range of homogeneity is between 0 and 1. If the image has little variation then homogeneity is high and if there is no variation then it is equal to 1
#OWLClass_010038
Homogenity1
#OWLClass_010039
The homogeneity feature measures the uniformity of the non-zero entries in the concurrence matrix. The range of homogeneity is between 0 and 1. If the image has little variation then homogeneity is high and if there is no variation then it is equal to 1. With respect to the feature homogeneity 1, the differences between neighbourd are squred weigthted
#OWLClass_010039
Homogenity2
#OWLClass_010040
The IMC1 is related to the entropy of the images and gives information on how a pixel value is correlated to its neighbourod
#OWLClass_010040
InformationalMeasureOfCorrelation1
#OWLClass_010041
The IMC2 is related to the entropy of the images and gives information on how a pixel value is correlated to its neighbourod
#OWLClass_010041
InformationalMeasureOfCorrelation2
#OWLClass_010042
Inverse Difference Moment (IDM) is the local homogeneity. It is high when local gray level is uniform and inverse GLCM is high
#OWLClass_010042
InverseDifferenceMomentNormalized
#OWLClass_010043
IDN measures image homogeneity as it assumes larger values for smaller gray tone differences in pair elements. It is more sensitive to the presence of near diagonal elements in the GLCM. It has maximum value when all elements in the image are same
#OWLClass_010043
InverseDifferenceNormalized
#OWLClass_010044
The inverse variance feature measures how the gray tone differences are distributed in pair elements
#OWLClass_010044
InverseVariance
#OWLClass_010045
The maximum probability extracts the maximum values from the GLCM
#OWLClass_010045
MaximumProbability
#OWLClass_010046
The sum average is the sum from all the averages from the GLCM
#OWLClass_010046
SumAverage
#OWLClass_010047
The sum entropy is the sum of all the entropies from the GLCM
#OWLClass_010047
SumEntropy
#OWLClass_010048
The sum variance is the sum of all the variances from the GLCM
#OWLClass_010048
SumVariance
#OWLClass_010049
The variance feature puts relatively high weights on the elements that differ from the average value in the GCLM
#OWLClass_010049
Variance_Concurrence
#OWLClass_010050
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.
#OWLClass_010050
ShortRunEmphasis
#OWLClass_010051
The LRE measures the distribution of long runs. The SRE is highly dependent on the occurrence of long runs and it gives high value for coarse structural textures
#OWLClass_010051
LongRunEmphasis
#OWLClass_010052
The GLN measures the similarity of gray level intensity values in the image. The GLN is low if the intensity values are alike
#OWLClass_010052
GrayLevelNonUniformity
#OWLClass_010053
The RLN measuresthe similarity of the length of runs throughout the image. The RLN is low if the run lengths are alike.
#OWLClass_010053
RunLenghtNonUniformity
#OWLClass_010054
The RP measures the homogeneity and the distribution of runs of an image in the direction. The RP isvery high if the all gray levels have the runs of length 1
#OWLClass_010054
RunPercentage
#OWLClass_010055
The LGLRE measures the distribution of low gray level values. The LGRE is high for the image with low gray level values
#OWLClass_010055
LowGrayLevelRunEmphasis
#OWLClass_010056
The HGLRE measures the distribution of high gray level values. The HGRE is high for the image with highgray level values
#OWLClass_010056
HighGrayLevelRunEmphasis
#OWLClass_010057
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
#OWLClass_010057
ShortRunLowGrayLevelEmphasis
#OWLClass_010058
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
#OWLClass_010058
ShortRunHighGrayLevelEmphasis
#OWLClass_010059
The LRLGLE measures the joint distribution of long runs and low gray level values. The LRLGE is high for the image with many long runs and low gray level values.
#OWLClass_010059
LongRunLowGrayLevelEmphasis
#OWLClass_010060
The LRHGLE measures the joint distribution of long runs and high gray level values. The LRHGE is high for images with many long runs and high gray level values
#OWLClass_010060
LongRunHighLevelEmphasis
#OWLClass_010061
This class is a container for most common image filters used to pre-process an image before feature extraction
#OWLClass_010061
ImagingFilters
#OWLClass_010062
This class includes all the filters which apply basic operations on one / multiple pixels of an image
#OWLClass_010062
PixelOperations
#OWLClass_010063
This class includes all the filters which are used to resize an image. They can be used both to increase or reduce the dimensions
#OWLClass_010063
Scaling
#OWLClass_010064
This class includes all the morphological and topological filters which can be applied to alter the geometry of an image
#OWLClass_010064
TopologicalOperations
#OWLClass_010065
This class includes all the filters which apply math operations between two / more different images
#OWLClass_010065
MathOperations
#OWLClass_010066
This class includes all the filters which apply affine transformations (e.g. rotations) which can be applied to an image
#OWLClass_010066
AffineTransformations
#OWLClass_010067
This class includes all the filters and most common kernels which are applied to smooth and modify the quality of an image
#OWLClass_010067
Smoothing
#OWLClass_010068
This class includes all the filters which are apply to decompose / transform an image according to some properties of its domain
#OWLClass_010068
TransformationAndDecompositions
#OWLClass_010069
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
#OWLClass_010069
HistoEqualization
#OWLClass_010070
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
#OWLClass_010070
MeanFiltering
#OWLClass_010071
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
#OWLClass_010071
MedianFiltering
#OWLClass_010072
This filter detects edges in different orientation and enhances them working on pixels with different orientations
#OWLClass_010072
EdgeEnhancement
#OWLClass_010073
This filter applies the same concept of the Histo Equalization filter, but to small, overlapping local areas of the image
#OWLClass_010073
LocalAreaHistoEqualization
#OWLClass_010074
This filter substitutes each original pixel value with its absolute value
#OWLClass_010074
AbsoluteFiltering
#OWLClass_010075
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
#OWLClass_010075
Erosion
#OWLClass_010076
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
#OWLClass_010076
Dilation
#OWLClass_010077
This filter performs morphological closing. The morphological operation includes an a dilation followed by an erosion, using the same structuring element for both operations
#OWLClass_010077
Closing
#OWLClass_010078
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
#OWLClass_010078
Opening
#OWLClass_010079
This filter performs morphological top-hat filtering. It computes the morphological opening and then substracts the results from the original image
#OWLClass_010079
TopHatFilter
#OWLClass_010080
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
#OWLClass_010080
NearestNeighbourInterpolation
#OWLClass_010081
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
#OWLClass_010081
BiLinearInterpolation
#OWLClass_010082
The filter makes use of third degree polynomial function to interpolate two pixels
#OWLClass_010082
BiCubicInterpolation
#OWLClass_010083
This filter uses a convolution kernel to interpolate the pixels of the input image. The kernel is based on the sampling function (sinc)
#OWLClass_010083
LanczosInterpolation
#OWLClass_010084
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
#OWLClass_010084
BellInterpolation
#OWLClass_010085
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
#OWLClass_010085
MitchellInterpolation
#OWLClass_010086
This filter uses an interpolant based not only on equation for the function values, but also for the derivatives
#OWLClass_010086
HermiteInterpolation
#OWLClass_010087
This filter performs interpolation using a B-spline of order n
#OWLClass_010087
BSpline
#OWLClass_010087
BSplineInterpolation
#OWLClass_010088
This filter performs the sum, pixel by pixel, of two different images
#OWLClass_010088
Sum
#OWLClass_010089
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
#OWLClass_010089
Subtraction
#OWLClass_010090
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
#OWLClass_010090
Average
#OWLClass_010091
This filter rotates an image according to a defined rotation matrix
#OWLClass_010091
Rotation
#OWLClass_010092
This filter shifts each pixel of the image according to a defined transformation vector
#OWLClass_010092
Translation
#OWLClass_010093
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
#OWLClass_010093
GaussianFilter
#OWLClass_010094
This filter applies a Gabor filter with a specified wavelength (in pixels) and orientation (in degrees)
#OWLClass_010094
GaborFilter
#OWLClass_010095
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
#OWLClass_010095
ButterwothFilter
#OWLClass_010096
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
#OWLClass_010096
SavitzkyGolayFilter
#OWLClass_010097
This filter decomposes an image into the space of the frequencies
#OWLClass_010097
FourierDecomposition
#OWLClass_010098
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
#OWLClass_010098
WaveletDecomposition
#OWLClass_010099
This class is a container for all the main algorithms used to segment / generate a ROI
#OWLClass_010099
SegmentationMethods
#OWLClass_010100
Manual Segmentation of a ROI, usually performed delineating the contour of the region in each slice
#OWLClass_010100
Manual
#OWLClass_010101
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
#OWLClass_010101
Semi-automated
#OWLClass_010102
Segmentation is atomated performed without requiring any additional interaction by the user. These tecnqiues usually make use of AI
#OWLClass_010102
Automated
#OWLClass_010103
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
#OWLClass_010103
ThresholdingBased
#OWLClass_010104
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
#OWLClass_010104
RegionBased
#OWLClass_010105
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
#OWLClass_010105
EdgeBased
#OWLClass_010106
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
#OWLClass_010106
ModelBased
#OWLClass_010107
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
#OWLClass_010107
GlobalThresholding
#OWLClass_010108
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
#OWLClass_010108
LocalAdaptiveThresholding
#OWLClass_010109
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
#OWLClass_010109
RegionGrowing
#OWLClass_010110
It is just opposite to region merging and whole image is continuously split until no further splitting of a region is possible
#OWLClass_010110
RegionSplitting
#OWLClass_010111
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
#OWLClass_010111
SplitAndMerge
#OWLClass_010112
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
#OWLClass_010112
WatershedAlgorithm
#OWLClass_010113
This method makes use of the laplacian operator (second order derivatives) to detect edges
#OWLClass_010113
LaplacianEdgeBased
#OWLClass_010114
This method makes use of the gradient operator (first order derivatives) to detect edges
#OWLClass_010114
GradientEdgeBased
#OWLClass_010115
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
#OWLClass_010115
MarrHildrethEdgeBased
#OWLClass_010116
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
#OWLClass_010116
HoughTransformBased
#OWLClass_010117
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
#OWLClass_010117
ActiveShape
#OWLClass_010118
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.
#OWLClass_010118
AppereanceModel
#OWLClass_010119
The implicit deformable models, also called implicit active contours or level sets, are designed to handle topological changes naturally
#OWLClass_010119
DeformableModel
#OWLClass_010120
The segmentation is performed trying to extract prior knowledge from a reference image often called atlas
#OWLClass_010120
AtlasModel
#OWLClass_010121
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
#OWLClass_010121
SupervisedMethods
#OWLClass_010122
In the unsupervised category we can put all the methods which does not require having labelled data because they are cluster based
#OWLClass_010122
UnsupervisedMethods
#OWLClass_010123
The most famous class of ANN. The netwotk needs to be trained on training data before being used. A list of features use by the net has to be defined
#OWLClass_010123
NeuralNetwork
#OWLClass_010124
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
#OWLClass_010124
SupportVectorMachine
#OWLClass_010125
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 cluste
#OWLClass_010125
KMean
#OWLClass_010126
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
#OWLClass_010126
CMeansFuzzy
#OWLClass_010127
A broad type for grouping physical and conceptual entities
#OWLClass_010127
http://purl.bioontology.org/ontology/STY
#OWLClass_010127
Entity
#OWLClass_010128
A broad type for grouping abstract entities or concepts
#OWLClass_010128
http://purl.bioontology.org/ontology/STY
#OWLClass_010128
ConceptualEntity
#OWLClass_010129
A broad type for grouping activities, processes and states
#OWLClass_010129
http://purl.bioontology.org/ontology/STY
#OWLClass_010129
Event
#OWLClass_010130
A process which occurs as a result of an activity
#OWLClass_010130
http://purl.bioontology.org/ontology/STY
#OWLClass_010130
Process
#OWLClass_010131
A process which is performed by a software or machine which elaborates some input data, transforms and processes them to get a certain output
#OWLClass_010131
CalculationRun
#OWLClass_010133
Data preprocessing describes any type of processing performed on raw data to prepare it for another processing procedure. Commonly used as a preliminary data mining practice
#OWLClass_010133
PreProcessing
#OWLClass_010134
A concept which involves the dimensions, quantity or capacity of something using some unit of measure, or which involves the quantitative comparison of entities
#OWLClass_010134
http://purl.bioontology.org/ontology/STY
#OWLClass_010134
QuantitativeConcept
#OWLClass_010135
A unit of measurement is a standardized quantity of a physical quality
#OWLClass_010135
http://purl.bioontology.org/ontology/UO
#OWLClass_010135
Unit
#OWLClass_010136
A unit which is a standard measure of the amount of a 2-dimensional flat surface
#OWLClass_010136
http://purl.bioontology.org/ontology/UO
#OWLClass_010136
AreaUnit
#OWLClass_010137
An area unit which is equal to an area enclosed by a square with sides each 1 meter long
#OWLClass_010137
http://purl.bioontology.org/ontology/UO
#OWLClass_010137
SquareMeter
#OWLClass_010138
An area unit which is equal to one thousand of square meter or 10^[-3] m^[2]
#OWLClass_010138
http://purl.bioontology.org/ontology/UO
#OWLClass_010138
SquareCentimeter
#OWLClass_010139
An area unit which is equal to one millionth of a square meter or 10^[-6] m^[2]
#OWLClass_010139
http://purl.bioontology.org/ontology/UO
#OWLClass_010139
SquareMillimiter
#OWLClass_010140
A unit which is a standard measure of the distance between two points
#OWLClass_010140
http://purl.bioontology.org/ontology/UO
#OWLClass_010140
LengthUnit
#OWLClass_010141
A length unit which is equal to one hundredth of a meter or 10^[-2] m
#OWLClass_010141
http://purl.bioontology.org/ontology/UO
#OWLClass_010141
Centimeter
#OWLClass_010142
A length unit which is equal to one thousandth of a meter or 10^[-3] m
#OWLClass_010142
http://purl.bioontology.org/ontology/UO
#OWLClass_010142
Millimiter
#OWLClass_010143
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
#OWLClass_010143
http://purl.bioontology.org/ontology/UO
#OWLClass_010143
Meter
#OWLClass_010144
A unit which is a standard measure of the amount of space occupied by any substance, whether solid, liquid, or gas
#OWLClass_010144
http://purl.bioontology.org/ontology/UO
#OWLClass_010144
VolumeUnit
#OWLClass_010145
A volume unit which is equal to one millionth of a cubic meter or 10^[-6] m^[3], or to 1 ml
#OWLClass_010145
http://purl.bioontology.org/ontology/UO
#OWLClass_010145
CubicCentimeter
#OWLClass_010147
An object perceptible to the sense of vision or touch or than can be used by an human
#OWLClass_010147
http://purl.bioontology.org/ontology/STY
#OWLClass_010147
PhysicalObject
#OWLClass_010148
A physical object made by human beings
#OWLClass_010148
ManufacturedObject
#OWLClass_010149
Computer software, or generally just software, is any set of machine-readable instructions (most often in the form of a computer program) that conform to a given syntax (sometimes referred to as a language) that is interpretable by a given processor and that directs a computer's processor to perform specific operations
#OWLClass_010149
http://purl.bioontology.org/ontology/SWO
#OWLClass_010149
Software
#OWLClass_010150
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
#OWLClass_010150
ProgrammingLanguage
#OWLClass_010151
Python
#OWLClass_010152
C++
#OWLClass_010153
C
#OWLClass_010154
Matlab
#OWLClass_010155
Python2.x
#OWLClass_010156
Python3.x
#OWLClass_010157
DevelopmentStatus
#OWLClass_010158
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
#OWLClass_010158
Alpha
#OWLClass_010159
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.
#OWLClass_010159
Beta
#OWLClass_010160
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
#OWLClass_010160
Live
#OWLClass_010161
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
#OWLClass_010161
License
#OWLClass_010162
Open source licenses are licenses that comply with the Open Source Definition — in brief, they allow software to be freely used, modified, and shared
#OWLClass_010162
OpenSource
#OWLClass_010165
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
#OWLClass_010165
Proprietary
#OWLClass_010166
Version
#OWLClass_010167
VersionName
#OWLClass_010168
VersionStatus
#OWLClass_010169
Software has developers actively maintaining it (fixing bugs)
#OWLClass_010169
Manteined
#OWLClass_010170
Sofware is no longer being supplied by the developers/publishers
#OWLClass_010170
Obsolete
#OWLClass_010172
A concept which is of interest because it pertains to the carrying out of a process or activity
#OWLClass_010172
http://purl.bioontology.org/ontology/STY
#OWLClass_010172
FunctionalConcept
#OWLClass_010174
http://purl.bioontology.org/ontology/SEDI
#OWLClass_010174
RTDose
#OWLClass_010176
http://purl.bioontology.org/ontology/SEDI
#OWLClass_010176
RTPlan
#OWLClass_010178
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
#OWLClass_010178
http://purl.bioontology.org/ontology/ROO
#OWLClass_010178
RadiationOncologyFunctionalConcept
#OWLClass_010179
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
#OWLClass_010179
http://purl.bioontology.org/ontology/ROO
#OWLClass_010179
OrganAtRisk
#OWLClass_010180
A Target Volume is a volume of tissue or of a geometrical concept forming the target for irradiation during radiation oncology
#OWLClass_010180
http://purl.bioontology.org/ontology/ROO
#OWLClass_010180
TargetVolume
#OWLClass_010181
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
#OWLClass_010181
http://purl.bioontology.org/ontology/ROO
#OWLClass_010181
PlanningTargetVolume
#OWLClass_010182
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
#OWLClass_010182
http://purl.bioontology.org/ontology/ROO
#OWLClass_010182
GrossTargetVolume
#OWLClass_010183
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)
#OWLClass_010183
http://purl.bioontology.org/ontology/NCIT
#OWLClass_010183
ImagingRegionOfInterest
#OWLClass_010184
The class of regions of interest used in Radiation Oncology and adhering to the Standardized Naming Conventions in Radiation Oncology
#OWLClass_010184
http://purl.bioontology.org/ontology/ROO
#OWLClass_010184
RadiationOncologyRegionOfInterest
#OWLClass_010185
A region of interest based on a delineation of an Organ-At-Risk (OAR)
#OWLClass_010185
http://purl.bioontology.org/ontology/ROO
#OWLClass_010185
OrganAtRisk(ROI)
#OWLClass_010186
The Total Energy is the value of Energy feature scaled by the volume of the voxel in cubic mm
#OWLClass_010186
TotalEnergy
#OWLClass_010187
A region of interest based on a delineation of a Target Volume
#OWLClass_010187
http://purl.bioontology.org/ontology/ROO
#OWLClass_010187
TargetVolume(ROI)
#OWLClass_010188
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)
#OWLClass_010188
http://purl.bioontology.org/ontology/ROO
#OWLClass_010188
CTV(ROI)
#OWLClass_010189
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)
#OWLClass_010189
http://purl.bioontology.org/ontology/ROO
#OWLClass_010189
GTV(ROI)
#OWLClass_010190
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)
#OWLClass_010190
http://purl.bioontology.org/ontology/ROO
#OWLClass_010190
PTV(ROI)
#OWLClass_010191
The statistical 10th percentile of the image matrix X
#OWLClass_010191
10Percentile
#OWLClass_010192
The statistical 90th percentile of the image matrix X
#OWLClass_010192
90Percentile
#OWLClass_010193
The interquartile range of the image matrix X is defined as the difference between the 75th and the 25th percentile
#OWLClass_010193
InterquartileRange
#OWLClass_010194
The Robust Mean Absolute Deviation is the mean distance of all intensity values from the Mean Value calculated on the subset of image array with gray levels in between, or equal to the 10th and 90th percentile
#OWLClass_010194
RobustMeanAbsoluteDeviation
#OWLClass_010195
The elongation of a shape which is defined as in http://dx.doi.org/10.1371/journal.pcbi.1000853
#OWLClass_010195
Elongation
#OWLClass_010196
The flatness of a shape, defined as in http://dx.doi.org/10.1371/journal.pcbi.1000853
#OWLClass_010196
Flatness
#OWLClass_010197
The largest pairwise euclidean distance between tumor surface voxels in the row-column plane
#OWLClass_010197
Maximum2DDiameterSlice
#OWLClass_010198
The largest pairwise euclidean distance between tumor surface voxels in the row-slice plane
#OWLClass_010198
Maximum2DDiameterColumn
#OWLClass_010199
The largest pairwise euclidean distance between tumor surface voxels in the column-slice plane
#OWLClass_010199
Maximum2DDiameterRow
#OWLClass_010200
Roundness is the measure of how closely the shape of an object approaches that of a mathematically perfect circle
#OWLClass_010200
Roundness
#OWLClass_010201
The mean gray level intensity of the i distribution
#OWLClass_010201
AverageIntensity
#OWLClass_010202
Difference Average measures the relationship between occurrences of pairs with similar intensity values and occurrences of pairs with differing intensity values.
#OWLClass_010202
DifferenceAverage
#OWLClass_010203
Difference Variance is a measure of heterogeneity that places higher weights on differing intensity level pairs that deviate more from the mean
#OWLClass_010203
DifferenceVariance
#OWLClass_010204
Sum of Squares or Variance is a measure in the distribution of neigboring intensity level pairs about the mean intensity level in the GLCM
#OWLClass_010204
SumSquares
#OWLClass_010205
Sum Variance is a measure of heterogeneity that places higher weights on neighboring intensity level pairs that deviate more from the mean
#OWLClass_010205
SumVariance
#OWLClass_010206
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
#OWLClass_010206
http://purl.bioontology.org/ontology/NCIT
#OWLClass_010206
Patient
#OWLClass_010207
A human being
#OWLClass_010207
http://purl.bioontology.org/ontology/NCIT
#OWLClass_010207
Person
#OWLClass_010208
Measures the similarity of gray-level intensity values in the image, where a lower GLNN value correlates with a greater similarity in intensity values. This is the normalized version of the GLN formula
#OWLClass_010208
GrayLevelNonUniformityNormalized
#OWLClass_010209
Measures the variance in gray level intensity for the runs
#OWLClass_010209
GrayLevelVariance
#OWLClass_010210
The Run Entropy (RE) value
#OWLClass_010210
RunEntropy
#OWLClass_010211
Measures the similarity of run lengths throughout the image, with a lower value indicating more homogeneity among run lengths in the image. This is the normalized version of the RLN formula
#OWLClass_010211
RunLengthNonUniformityNormalized
#OWLClass_010212
Measures the variance in gray level intensity for the runs
#OWLClass_010212
RunVariance
#OWLClass_010213
Any record of a medical imaging event whether physical or electronic
#OWLClass_010213
http://purl.bioontology.org/ontology/NCIT
#OWLClass_010213
MedicalImage
#OWLClass_010214
The data or image obtained by gathering information with a sensing device
#OWLClass_010214
http://purl.bioontology.org/ontology/NCIT
#OWLClass_010214
Scan
#OWLClass_010215
All the properties used to define a SW
#OWLClass_010215
SoftwareProperties
#OWLClass_010216
Features based on the computation of the Gray Level Size Zone Matrix (GLSZM). It quantifies gray level zones in an image. A gray level zone is defined as a the number of connected voxels that share the same gray level intensity. A voxel is considered connected if the distance is 1 according to the infinity norm. This yields a 26-connected region in a 3D image, and an 8-connected region in a 2D image
#OWLClass_010216
GrayLevelSizeZoneMatrixBased
#OWLClass_010217
Measures the variance in gray level intensities for the zones
#OWLClass_010217
GrayLevelVariance_GLZSM
#OWLClass_010218
Properties and characteristics related to any manufactered object
#OWLClass_010218
ManufacteredObjectProperties
#OWLClass_010219
HighIntensityEmphasis
#OWLClass_010220
A measure of the distribution of large area size zones, with a greater value indicative of more larger size zones and more coarse textures
#OWLClass_010220
LargeAreaEmphasis
#OWLClass_010221
A measure of the distribution of small size zones, with a greater value indicative of more smaller size zones and more fine textures
#OWLClass_010221
SmallAreaEmphasis
#OWLClass_010222
Measures the coarseness of the texture by taking the ratio of number of zones and number of voxels in the ROI
#OWLClass_010222
ZonePercentage
#OWLClass_010223
Measures the variance in gray level intensities for the zones
#OWLClass_010223
GrayLevelVariance_GLSZM
#OWLClass_010224
Measures the variance in zone size volumes for the zones
#OWLClass_010224
ZoneVariance
#OWLClass_010225
Measures the uncertainty/randomness in the distribution of zone sizes and gray levels. A higher value indicates more heterogeneneity in the texture patterns
#OWLClass_010225
ZoneEntropy
#OWLClass_010226
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
#OWLClass_010226
LowGrayLevelZoneEmphasis
#OWLClass_010227
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.
#OWLClass_010227
HighGrayLevelZoneEmphasis
#OWLClass_010228
Measures the proportion in the image of the joint distribution of smaller size zones with lower gray-level values
#OWLClass_010228
SmallAreaLowGrayLevelEmphasis
#OWLClass_010229
Measures the proportion in the image of the joint distribution of smaller size zones with higher gray-level values
#OWLClass_010229
SmallAreaHighGrayLevelEmphasis
#OWLClass_010230
Measures the proportion in the image of the joint distribution of larger size zones with lower gray-level values
#OWLClass_010230
LargeAreaLowGrayLevelEmphasis
#OWLClass_010231
Measures the proportion in the image of the joint distribution of larger size zones with higher gray-level values
#OWLClass_010231
LargeAreaHighGrayLevelEmphasis
#OWLClass_010232
HighIntensityLargeAreaEmphasis
#OWLClass_010233
HighIntensitySmallAreaEmphasis
#OWLClass_010234
IntensityVariability
#OWLClass_010235
IntensityVariabilityNormalized
#OWLClass_010236
LargeAreaEmphasis
#OWLClass_010237
LowIntensityLargeAreaEmphasis
#OWLClass_010238
LowIntensitySmallAreaEmphasis
#OWLClass_010239
SizeZoneVariability
#OWLClass_010240
A concept which is of interest because it is related to DICOM objects used in radiation oncology
#OWLClass_010240
RadiationOncologyDICOMFunctionalConcept
#OWLClass_010241
http://purl.bioontology.org/ontology/SEDI
#OWLClass_010241
RTStrcutureSet
#OWLClass_010246
A volume unit which is equal to 10^[-9] m^[3]
#OWLClass_010246
CubicMillimeter
#OWLClass_010247
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
#OWLClass_010247
http://purl.bioontology.org/ontology/UO
#OWLClass_010247
CubicMeter
#OWLDataProperty_010191
has_value
#OWLNamedIndividual_010221
C++
#OWLNamedIndividual_010222
C
#OWLNamedIndividual_010223
Python
#OWLNamedIndividual_010224
Matlab
#OWLNamedIndividual_010225
Alpha
#OWLNamedIndividual_010226
Beta
#OWLNamedIndividual_010227
Live
#OWLNamedIndividual_010228
Manteined
#OWLNamedIndividual_010229
Obsolete
#OWLNamedIndividual_010230
OpenSource
#OWLNamedIndividual_010231
Proprietary
#OWLNamedIndividual_010232
SquareCentimeter
#OWLNamedIndividual_010233
SquareMeter
#OWLNamedIndividual_010234
SquareMillimiter
#OWLNamedIndividual_010235
Centimeter
#OWLNamedIndividual_010236
Millimiter
#OWLNamedIndividual_010237
Meter
#OWLNamedIndividual_010238
CubicCentimeter
#OWLObjectProperty_010192
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
#OWLObjectProperty_010192
has_version_name
#OWLObjectProperty_010193
has_version_status
#OWLObjectProperty_010194
This predicate is used to link the name of the version with the corresponding SW
#OWLObjectProperty_010194
is_version_of
#OWLObjectProperty_010195
This predicate is used to describe which is the programming language in which a SW or application was written
#OWLObjectProperty_010195
has_programming_language
#OWLObjectProperty_010196
is_programming_language_of
#OWLObjectProperty_010197
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
#OWLObjectProperty_010197
has_license
#OWLObjectProperty_010198
has_unit
#OWLObjectProperty_010199
Related in time by preceding, co-occuring with, or following
#OWLObjectProperty_010199
temporally_related_to
#OWLObjectProperty_010200
Occurs earlier in time. This includes antedates, comes before, is in advance of, predates, and is prior to
#OWLObjectProperty_010200
precedes
#OWLObjectProperty_010201
follows
#OWLObjectProperty_010202
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
#OWLObjectProperty_010202
has_segmentation_method
#OWLObjectProperty_010205
This predicates is used to link a SW with its developed status, which specifies what is the status of the development of the SW from first developed test versions to live (in a production environment) versions
#OWLObjectProperty_010205
has_development_status
#OWLObjectProperty_010208
has_pacs_study
#OWLObjectProperty_010210
has_computed
#OWLObjectProperty_010211
was_computed_by
#OWLObjectProperty_010212
has_filter
#OWLObjectProperty_010215
has_calculation_run
#OWLObjectProperty_010216
has_pre_processing
#OWLObjectProperty_010217
has_radiomics_feature
#OWLObjectProperty_010218
has_delineation
#OWLObjectProperty_010219
Relation between a delineated contour and the corresponding entity
#OWLObjectProperty_010219
delineation_of
#OWLObjectProperty_010242
is_connected_to
#OWLObjectProperty_010243
Created from a certain context
#OWLObjectProperty_010243
originated_from
#OWLObjectProperty_010244
is_radiomics_feature_of
#OWLObjectProperty_010245
To put into effect according to or by means of a definite procedure
#OWLObjectProperty_010245
implemented_in