Related in time by preceding, co-occuring with, or following
Refers to the process / tool used to produce the computed entity
Related to a computational process (calculation run)
Specifies what is the status of the development of the SW from first developed test versions to live (in a production environment) versions
Specifies the direction / orientation of a filter
Specifies the intensity fraction (refers to class IntensityFraction) used to compute the volume at intensity fraction
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
Specifies any post processing applied to an image
This predicate is used to describe which is the programming language in which a SW or application was written
Specifies the radiomic feature related to the entity
Speficies if a particular scale (e.g. logaritmic has been applied)
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
Specifies the unity associated to a quantity
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
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
To put into effect according to or by means of a definite procedure
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
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
The data or image obtained by gathering information with a sensing device
Any record of a medical imaging event whether physical or electronic
A measurement that uses a mathematical transformation of a physical quantity instead of the quantity itself
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)
A broad type for grouping activities, processes and states
A broad type for grouping physical and conceptual entities
An object perceptible to the sense of vision or touch or than can be used by an human
A broad type for grouping abstract entities or concepts
A concept which involves the dimensions, quantity or capacity of something using some unit of measure, or which involves the quantitative comparison of entities
A concept which is of interest because it pertains to the carrying out of a process or activity
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
A unit of measurement is a standardized quantity of a physical quality
A unit which is a standard measure of the distance between two points
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
A length unit which is equal to one hundredth of a meter or 10^[-2] m
A length unit which is equal to one thousandth of a meter or 10^[-3] m
A unit which is a standard measure of the amount of a 2-dimensional flat surface
An area unit which is equal to an area enclosed by a square with sides each 1 meter long
An area unit which is equal to one thousand of square meter or 10^[-3] m^[2]
An area unit which is equal to one millionth of a square meter or 10^[-6] m^[2]
A unit which is a standard measure of the amount of space occupied by any substance, whether solid, liquid, or gas
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
A volume unit which is equal to one millionth of a cubic meter or 10^[-6] m^[3], or to 1 ml
A unit which is a standard measure of the number of repetitive actions in a particular time
A frequency unit which is equal to 1 complete cycle of a recurring phenomenon in 1 second
A unit which is a standard measure of the figure or space formed by the junction of two lines or planes.
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.
A plane angle unit which is equal to 1/360 of a full rotation or 1.7453310^[-2] rad.
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)
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)
The class of regions of interest used in Radiation Oncology and adhering to the Standardized Naming Conventions in Radiation Oncology
A region of interest based on a delineation of a Target Volume
A region of interest based on a delineation of an Organ-At-Risk (OAR)
A concept which is of interest because it is related to DICOM objects used in radiation oncology
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)
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
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
A Target Volume is a volume of tissue or of a geometrical concept forming the target for irradiation during radiation oncology
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
Development status is an information content entity which indicates the maturity of a sofrware entity within the context of the software life cycle.
Alpha is a development status which is applied to software by the developer/publisher during initial development and testing. Software designated alpha is commonly unstable and prone to crashing. It may or may not be released publicly
Beta is a development status which is generally applied to software by the developer/publisher once the majority of features have been implemented, but when the software may still contain bugs or cause crashes or data loss. Software designated beta is often released publicly, either on a general release or to a specific subset of users called beta testers.
Live 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
Sofware is no longer being supplied by the developers/publishers
Software has developers actively maintaining it (fixing bugs)
This feature emphasises runs in the upper right quadrant of the GLDZM, where large zone distances and low grey levels are located.
Contrast assesses grey level variations. It is defines as in https://doi.org/10.5589/m02-004
The cluster prominence is a measure of asymmetry. When the cluster prominence value is high, the image is less symmetric
This filter performs interpolation using a B-spline of order n
Most common discretised grey level present in the histogram
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
This class includes all the filters which are used to resize an image. They can be used both to increase or reduce the dimensions
The uniformity is a measure of the randomness of the grey levels distirbution histogram
Compactness 2 is anothere measure to describe how sphere-like the volume is
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
This feature estimates the variance in zone counts for the grey levels
The surface area is calculated from the ROI mesh, by summing over the face area surfaces
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
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.
Defined as second moment in Sun and Wee (1983)
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
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
Convolutes with a laplacian kernel the original image
The difference between grey levels at two different fractional volumes
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
The intensity Histogram Coefficient of Variation measures the dispersion of the histogram
Measure of dispersion from the mean of the histogram
The variance for the diagonal probabilities
The difference between the volume fractions at two different intensity fractions. See also https://doi.org/10.1016/j.patcog.2008.08.011
The cluster tendency indicates into how many clusters the gray levels present in the image can be classified
This feature emphasises runs in the lower left quadrant of the GLDZM, where small zone distances and high grey levels are located
This feature estimates the variance in dependence counts for the different dependence counts possible
The inverse variance feature measures how the gray tone differences are distributed in pair elements
The variance from grey level distribution
Difference between the intensity at volume fraction 10 and intensity at volume fraction 90
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
Nearest neighbour interpolation assigns grey levels in the output grid to the values of the closest voxels in the input grid.
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
The HGLRE measures the distribution of high gray level values. The HGRE is high for the image with highgray level values
The "ordinary" straight-line distance between two points in Euclidean space
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
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
The Standard Deviation measures the amount of variation or dispersion from the Mean Value
Properties related to the process used to perform a calculation (e.g. a SW processing something)
Probability corresponding to the most common grey level co-occurence in the GCLM
This filter performs morphological closing. The morphological operation includes an a dilation followed by an erosion, using the same structuring element for both operations
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.
The space separating two objects or points.
Complex textures are non-uniform and rapid changes in grey levels are common
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
This feature emphasises runs in the lower left quadrant of the GLSZM, where small zone sizes and high grey levels are located
This is the normalised version of the zone distance non-uniformity feature
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
This is the normalised version of the run length non-uniformity feature
This feature emphasises high neighbouring grey level dependence counts
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
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
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
This feature emphasises runs in the upper right quadrant of the GLRLM, where long run lengths and low grey levels are located
This filter detects edges in different orientation and enhances them working on pixels with different orientations
This feature emphasises runs in the lower right quadrant of the GLSZM, where large zone sizes and high grey levels are located
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
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
The image filter space is a container for all the filters that can be applied during the computation of a feature
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
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
Orientation referred to angular directions
In the fixed bin number method, grey levels are discretised to a fixed number of bins. The number of bins should always be specified
This class is a container for most common image filters used to pre-process an image before feature extraction
This filter rotates an image according to a defined rotation matrix
The skewness measures the degree of histogram of gray levels asymmetry around the central value
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
This feature emphasises runs in the lower right quadrant of the GLDZM, where large zone distances and high grey levels are located
Spherical disproportion is a measure to describe how sphere-like the volume is
Segmentation is automated performed without requiring any additional interaction by the user. These tecnqiues usually make use of AI
The maximum 3D diameter is the distance between the two most distant vertices in the ROI mesh vertices sets
This class is a container for all the main algorithms used to segment / generate a ROI
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
The distance between two points is the sum of the absolute differences of their Cartesian coordinates.
The filter makes use of third degree polynomial function to interpolate two pixels
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
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
Moran’s I index is an indicator of spatial autocorrelation. See also https://doi.org/10.1093/biomet/37.1-2.17
Median absolute deviation is similar in concept to mean absolute deviation, but measures dispersion from the median instead of mean
The energy is the square sum of all the gray levels associated to an image
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
Normalized inverse difference as suggested in https://doi.org/10.5589/m02-004
The correlation feature shows the linear dependence of gray level values in the cooccurence matrix
Geary’s C measures spatial autocorrelation. See also https://doi.org/10.2307%2F2986645
Textures with large changes in grey levels between neighbouring voxels are called busy
The entropy for the diagonal probabilities
Cubic convolution uses the same procedures as cubic spline (refers CubicSpline), but approximates the solution using a convolution filter.
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
This method makes use of the gradient operator (first order derivatives) to detect edges
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
This method makes use of the laplacian operator (second order derivatives) to detect edges
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
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
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
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
The segmentation is performed trying to extract prior knowledge from a reference image often called atlas
The implicit deformable models, also called implicit active contours or level sets, are designed to handle topological changes naturally
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
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
It is just opposite to region merging and whole image is continuously split until no further splitting of a region is possible
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
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
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
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
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
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
Manual Segmentation of a ROI, usually performed delineating the contour of the region in each slice
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
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
In the unsupervised category we can put all the methods which does not require having labelled data because they are cluster based
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
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
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
The image space contains information about an image
The variance for the cross-diagonal probabilities
Binary mask as result of a segmentation process
This is a normalised version of the dependence count non-uniformity feature
This filter applies a Gabor filter with a specified wavelength (in pixels) and orientation (in degrees)
This is the normalised version of the grey level non-uniformity feature
Container for parameters and computation methods used to derive features
An organization founded for a religious, educational, professional, or social purpose
Properties and characteristics related to any manufactered object
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
Open source licenses are licenses that comply with the Open Source Definition — in brief, they allow software to be freely used, modified, and shared
Proprietary 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
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
The entropy for the cross-diagonal probabilities
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
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
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
This filter performs the sum, pixel by pixel, of two different images
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.
This filter applies the same concept of the Histo Equalization filter, but to small, overlapping local areas of the image
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
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.
The mean grey level from grey level distribution
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
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
Sphericity is a further measure to describe how sphere-like the volume is
The statistical 10th percentile of the grey level distribution
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.
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
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
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
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
The IMC1 is related to the entropy of the images and gives information on how a pixel value is correlated to its neighbourod
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
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
The volume V is calculated from the ROI mesh as indicated in https://doi.org/10.1109/ICIP.2001.958278
This feature emphasises runs in the upper left quadrant of the GLDZM, where small zone distances and low grey levels are located
Instead of small zone distances, low grey levels are emphasised
This filter uses a convolution kernel to interpolate the pixels of the input image. The kernel is based on the sampling function (sinc)
The interquartile range (IQR) is defined as P75 - P25, where P75 is the 75th percentile of the image matrix, and P25 is the 25th
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
The ratio of the difference between the 75 and 25 percentile and their sum
This class includes all the filters which apply basic operations on one / multiple pixels of an image
This feature emphasises low neighbouring grey level dependence counts
Difference between the volume at intensity fraction 10 and volume at intensity fraction 90
The Lloyd-Max algorithm is an iterative clustering method that seeks to minimise mean squared discretisation errors (Max, 1960; Lloyd, 1982).
Merged directions per slice and then averaged
Features which are directly derived from imaging properties like for example the SUV (Standard Uptake Volume) for PET
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
This feature estimates the variance in runs for run lengths
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
Instead of low neighbouring grey level dependence counts, low grey levels are emphasised
It is the Shannon entropy of the histogram
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
As defined in http://dx.doi.org/10.1109/TSMC.1973.4309314
This class includes all the filters which apply affine transformations (e.g. rotations) which can be applied to an image
The statistical features describe how grey levels within the region of interest (ROI) are distributed
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
Also called sum of squares as in http://dx.doi.org/10.1109/TSMC.1973.4309314
Total Energy is the value of Energy feature scaled by the volume of the voxel in cubic mm
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
The minimum intensity presents in at most 10% of the volume
No weigths are applied to the computed distances
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
The LGLRE measures the distribution of low gray level values. The LGRE is high for the image with low gray level values
This is a normalised version of the zone size non-uniformity feature
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
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.
The minimum gradient of the grey level histogram
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
The LRE measures the distribution of long runs
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.
The minimum intensity presents in at most 90% of the volume
This filter performs morphological top-hat filtering. It computes the morphological opening and then substracts the results from the original image
Same as the inverse difference feature, but with lower weigths for elements that are further from the diagonal
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
Difference between the 75 and 25 interquartiles of the histogram
The intensity histogram robust mean absolute deviation is the mean restricted to grey values closer to the center of the distribution
This filter uses an interpolant based not only on equation for the function values, but also for the derivatives
Linear interpolation makes use of first order polynomial
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
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
This is a normalised version of the grey level non-uniformity feature
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.
This feature emphasises runs in the upper right quadrant of the GLSZM, where large zone sizes and low grey levels are located
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
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
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
This class includes all the filters which apply math operations between two / more different images
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
The average for the cross-diagonal probabilities
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
The textural features describe patterns or the spatial distribution of voxel intensities
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
These features are based on the intensity histogram, whch is generated by discretising the original set of grey levels into grey level bins
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
The maximum percentage volume with at least (90% of the max intensity)
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
This feature estimates the variance in dependence counts for the grey levels.
Normalized version of the inverse difference moment, as suggested by https://doi.org/10.5589/m02-004
Feature defined in http://dx.doi.org/10.1371/journal.pone.0093600
Specifies how texture matrix are computed and aggregated
Same feature as defined in https://doi.org/10.1016/j.radonc.2016.07.007
Cubic spline interpolation draw upon a larger neighbourhood to evaluate a smooth, continuous third-order polynomial at the points of the output grid.
The range of grey levels is the difference between the maximum and the minimum
The surface to volume ratio is the ratio between the Surface and the Volume
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.
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
Asphericity describes how much the ROI deviates from a perfect sphere
This feature is depracated. Same as Inverse Difference Moment
Tangent trigonometric Function. Ratio between sine and cosine
Specify direction for example for a filter
This feature emphasises runs in the lower right quadrant of the GLRLM, where long run lengths and high grey levels are located
Highest discretized grey level in the histogram distribution
This feature estimates the variance in zone counts for the different zone sizes
Weigthening the distance by using a certain function (e.g. exponential)
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
The mean of the absolute deviations of all voxel intensities around the mean intensitity value
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
Histogram median absolute deviation is conceptually similar to histogram mean absolute deviation, but measures dispersion from the median instead of mean
Maximum 2D diameter (Slice) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-column (generally the axial) plane.
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
Maximum 2D diameter (Column) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the row-slice (usually the coronal) plane.
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
This feature emphasises runs in the upper left quadrant of the GLSZM, where small zone sizes and low grey levels are located
Difference between maximum and minimum of the histogram
The square root of the arithmetic mean of the squares of the values
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
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
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
The grey level weigthed sum of joint probabilities
A volume unit which is equal to 10^[-9] m^[3]
Contrast depends on the dynamic range of the grey levels as well as the spatial frequency of intensity changes
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).
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
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
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
Coefficient of variation is the ratio between the standard deviation and the mean value
This feature estimates the variance in zone counts for the different zone distances
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).
This feature estimates the variance in runs for the grey levels
The statistical 90th percentile of the grey level distribution
The discretized level corresponding to the maximum histogram gradient
Grey level discretisation or quantisation of the ROI is often required to make calculation of texture features tractable (Yip and Aerts, 2016)
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
The maximum percentage volume with at least (10% of the max intensity)
It represents the energy of the probability matrix. Please note that this feature is also called Energy or Uniformity.
Feature as defined in https://doi.org/10.1007/s00259-011-1845-6
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
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
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
Maximum 2D diameter (Row) is defined as the largest pairwise Euclidean distance between tumor surface voxels in the column-slice (usually the sagittal) plane.
Local intensity features consider voxel intensities within a defined neighbourhood around a center voxel
The intensity fraction "I" for grey level "i" in the range G is: I = (i-min(G)) / (max(G) - min(G))
This filter substitutes each original pixel value with its absolute value
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
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