nyxus 0.1.2

Scalable library for calculating features from intensity-label image data

Nyxus

A scalable feature extractor

Overview

The feature extraction plugin extracts morphology and intensity based features from pairs of intensity/binary mask images and produces a csv file output. The input image should be in tiled OME TIFF format. The plugin extracts the following features:

Nyxus provides a set of pixel intensity, morphology, texture, intensity distribution features, digital filter based features and image moments

---

Nyxus feature code	Description
INTEGRATED_INTENSITY	Integrated intensity of the region of interest (ROI)
MEAN, MAX, MEDIAN, STANDARD_DEVIATION, MODE	Mean/max/median/stddev/mode intensity value of the ROI
SKEWNESS, KURTOSIS, HYPERSKEWNESS, HYPERFLATNESS	higher standardized moments
MEAN_ABSOLUTE_DEVIATION	Mean absolute devation
ENERGY	ROI energy
ROOT_MEAN_SQUARED	Root of mean squared deviation
ENTROPY	ROI entropy - a measure of the amount of information in the ROI
UNIFORMITY	Uniformity - measures how uniform the distribution of ROI intensities is
UNIFORMITY_PIU	Percent image uniformity, another measure of intensity distribution uniformity
P01, P10, P25, P75, P90, P99	1%, 10%, 25%, 75%, 90%, and 99% percentiles of intensity distribution
INTERQUARTILE_RANGE	Distribution's interquartile range
ROBUST_MEAN_ABSOLUTE_DEVIATION	Robust mean absolute deviation
MASS_DISPLACEMENT	ROI mass displacement
AREA_PIXELS_COUNT	ROI area in the number of pixels
COMPACTNESS	Mean squared distance of the object’s pixels from the centroid divided by the area
BBOX_YMIN	Y-position and size of the smallest axis-aligned box containing the ROI
BBOX_XMIN	X-position and size of the smallest axis-aligned box containing the ROI
BBOX_HEIGHT	Height of the smallest axis-aligned box containing the ROI
BBOX_WIDTH	Width of the smallest axis-aligned box containing the ROI
MAJOR/MINOR_AXIS_LENGTH, ECCENTRICITY, ORIENTATION, ROUNDNESS	Inertia ellipse features
NUM_NEIGHBORS, PERCENT_TOUCHING	The number of neighbors bordering the ROI's perimeter and related neighbor methods
EXTENT	Proportion of the pixels in the bounding box that are also in the region
CONVEX_HULL_AREA	Area of ROI's convex hull
SOLIDITY	Ratio of pixels in the ROI common with its convex hull image
PERIMETER	Number of pixels in ROI's contour
EQUIVALENT_DIAMETER	Diameter of a circle with the same area as the ROI
EDGE_MEAN/MAX/MIN/STDDEV_INTENSITY	Intensity statistics of ROI's contour pixels
CIRCULARITY	Represents how similar a shape is to circle. Clculated based on ROI's area and its convex perimeter
EROSIONS_2_VANISH	Number of erosion operations for a ROI to vanish in its axis aligned bounding box
EROSIONS_2_VANISH_COMPLEMENT	Number of erosion operations for a ROI to vanish in its convex hull
FRACT_DIM_BOXCOUNT, FRACT_DIM_PERIMETER	Fractal dimension features
GLCM	Gray level co-occurrence Matrix features
GLRLM	Gray level run-length matrix based features
GLSZM	Gray level size zone matrix based features
GLDM	Gray level dependency matrix based features
NGTDM	Neighbouring gray tone difference matrix features
ZERNIKE2D, FRAC_AT_D, RADIAL_CV, MEAN_FRAC	Radial distribution features
GABOR	A set of Gabor filters of varying frequencies and orientations

For the complete list of features see Nyxus provided features

Feature groups

Apart from defining your feature set by explicitly specifying comma-separated feature code, Nyxus lets a user specify popular feature groups. Supported feature groups are:

---

Group code	Belonging features
*all_intensity*	integrated_intensity, mean, median, min, max, range, standard_deviation, standard_error, uniformity, skewness, kurtosis, hyperskewness, hyperflatness, mean_absolute_deviation, energy, root_mean_squared, entropy, mode, uniformity, p01, p10, p25, p75, p90, p99, interquartile_range, robust_mean_absolute_deviation, mass_displacement
*all_morphology*	area_pixels_count, area_um2, centroid_x, centroid_y, weighted_centroid_y, weighted_centroid_x, compactness, bbox_ymin, bbox_xmin, bbox_height, bbox_width, major_axis_length, minor_axis_length, eccentricity, orientation, num_neighbors, extent, aspect_ratio, equivalent_diameter, convex_hull_area, solidity, perimeter, edge_mean_intensity, edge_stddev_intensity, edge_max_intensity, edge_min_intensity, circularity
*basic_morphology*	area_pixels_count, area_um2, centroid_x, centroid_y, bbox_ymin, bbox_xmin, bbox_height, bbox_width
*all_glcm*	glcm_angular2ndmoment, glcm_contrast, glcm_correlation, glcm_variance, glcm_inversedifferencemoment, glcm_sumaverage, glcm_sumvariance, glcm_sumentropy, glcm_entropy, glcm_differencevariance, glcm_differenceentropy, glcm_infomeas1, glcm_infomeas2
*all_glrlm*	glrlm_sre, glrlm_lre, glrlm_gln, glrlm_glnn, glrlm_rln, glrlm_rlnn, glrlm_rp, glrlm_glv, glrlm_rv, glrlm_re, glrlm_lglre, glrlm_hglre, glrlm_srlgle, glrlm_srhgle, glrlm_lrlgle, glrlm_lrhgle
*all_glszm*	glszm_sae, glszm_lae, glszm_gln, glszm_glnn, glszm_szn, glszm_sznn, glszm_zp, glszm_glv, glszm_zv, glszm_ze, glszm_lglze, glszm_hglze, glszm_salgle, glszm_sahgle, glszm_lalgle, glszm_lahgle
*all_gldm*	gldm_sde, gldm_lde, gldm_gln, gldm_dn, gldm_dnn, gldm_glv, gldm_dv, gldm_de, gldm_lgle, gldm_hgle, gldm_sdlgle, gldm_sdhgle, gldm_ldlgle, gldm_ldhgle
*all_ngtdm*	ngtdm_coarseness, ngtdm_contrast, ngtdm_busyness, ngtdm_complexity, ngtdm_strength
*all*	All the features

Example: running Nyxus to extract only intensity and basic morphology features

./nyxus --features=*all_intensity*,*basic_morphology* --intDir=/home/ec2-user/data-ratbrain/int --segDir=/home/ec2-user/data-ratbrain/seg --outDir=/home/ec2-user/work/OUTPUT-ratbrain --filePattern=.* --csvFile=singlecsv 

Installation

Nyxus can be installed as a Python package, as a Docker image, or be compiled from source

Install with pip

You can install Nyxus using pip package manager:

pip install nyxus 

Install from sources

Another option is cloning the Nyxus repository and installing it manually:

git clone https://github.com/friskluft/nyxus.git
cd nyxus
cmake . 

Install from sources and package into a Docker image

The 3rd option alternative to running Nyxus as a Python library or a standalone executable program is making a POLUS plugin of it by packaging it into a Docker image. The latter requires advancing the plugin version number in file VERSION, building the Docker image, uploading it to POLUS repository, and registering the plugin. To build an image, run

./build-docker.sh

This feature extractor is designed to be run on POLUS WIPP platform but dry-running it on a local machine before deployment to WIPP after code change is a good idea

Example - testing docker image with local data

Assuming the Docker image's hash is <hash>, the root of the data directory on the test machine is /images/collections, and intensity and segmentation mask image collections are in subdirectories /images/collections/c1/int and /images/collections/c1/seg respectively, the image can be test-run with command

docker run -it --mount type=bind,source=/images/collections,target=/data <hash> --intDir=/data/c1/int --segDir=/data/c1/seg --outDir=/data/output --filePattern=.* --csvfile=separatecsv --features=all

Assuming the built image's version as displayed by command

docker images

is "labshare/polus-feature-extraction-plugin:1.2.3", the image can be pushed to POLUS organization repository at Docker image cloud with the following 2 commands. The first command

docker tag labshare/polus-feature-extraction-plugin:1.2.3 polusai/polus-feature-extraction-plugin:1.2.3

aliases the labshare organization image in a different organization - polusai - permitting image's registering as a POLUS WIPP plugin. The second command

docker push polusai/polus-feature-extraction-plugin:1.2.3

uploads the image to the repository of WIPP plugin images. Lastly, to register the plugin in WIPP, edit the text file of plugin's manifest (file plugin.json) to ensure that the manifest keys version and containerId refer to the uploaded Docker image version, navigate to WIPP web application's plugins page, and add a new plugin by uploading the updated manifest file.

Dependencies

Nyxus is tested with Python 3.7+. Building Nyxus from sources requires the following packages:

NIST Hedgehog >= 1.0.16
NIST Fastloader >= 2.1.4
pybind11 >= 2.8.1
libTIFF >= 3.6.1

Plugin inputs

Label image collection: The input should be a labeled image in tiled OME TIFF format (.ome.tif). Extracting morphology features, Feret diameter statistics, neighbors, hexagonality and polygonality scores requires the segmentation labels image. If extracting morphological features is not required, the label image collection can be not specified.

Intensity image collection: Extracting intensity-based features requires intensity image in tiled OME TIFF format. This is an optional parameter - the input for this parameter is required only when intensity-based features needs to be extracted.

File pattern: Enter file pattern to match the intensity and labeled/segmented images to extract features (https://pypi.org/project/filepattern/) Filepattern will sort and process files in the labeled and intensity image folders alphabetically if universal selector(.*.ome.tif) is used. If a more specific file pattern is mentioned as input, it will get matches from labeled image folder and intensity image folder based on the pattern implementation.

Pixel distance: Enter value for this parameter if neighbors touching cells needs to be calculated. The default value is 5. This parameter is optional.

Features: Comma separated list of features to be extracted. If all the features are required, then choose option all.

Csvfile: There are 2 options available under this category. Separatecsv - to save all the features extracted for each image in separate csv file. Singlecsv - to save all the features extracted from all the images in the same csv file.

Embedded pixel size: This is an optional parameter. Use this parameter only if units are present in the metadata and want to use those embedded units for the features extraction. If this option is selected, value for the length of unit and pixels per unit parameters are not required.

Length of unit: Unit name for conversion. This is also an optional parameter. This parameter will be displayed in plugin's WIPP user interface only when embedded pixel size parameter is not selected (ckrresponding check box checked).

Pixels per unit: If there is a metric mentioned in Length of unit, then Pixels per unit cannot be left blank and hence the scale per unit value must be mentioned in this parameter. This parameter will be displayed in plugin's user interface only when embedded pixel size parameter is not selected.

Note: If Embedded pixel size is not selected and values are entered in Length of unit and Pixels per unit, then the metric unit mentioned in length of unit will be considered. If Embedded pixel size, Length of unit and Pixels per unit is not selected and the unit and pixels per unit fields are left blank, the unit will be assumed to be pixels.

Output: The output is a csv file containing the value of features required.

For more information on WIPP, visit the official WIPP page.

Plugin command line parameters

Running the WIPP feature extraction plugin is controlled via nine named input arguments and one output argument that are passed by WIPP web application to plugin's Docker image - see Table 1.

Table 1 - Command line parameters

---

Parameter	Description	I/O	Type
--intDir	Intensity image collection	Input	collection
--segDir	Labeled image collection	Input	collection
--intSegMapDir	Data collection of the ad-hoc intensity-to-mask file mapping	Input	Collection
--intSegMapFile	Name of the text file containing an ad-hoc intensity-to-mask file mapping. The files are assumed to reside in corresponding intensity and label collections	Input	string
--features	Select intensity and shape features required	Input	array
--filePattern	To match intensity and labeled/segmented images	Input	string
--csvfile	Save csv file as one csv file for all images or separate csv file for each image	Input	enum
--pixelDistance	Pixel distance to calculate the neighbors touching cells	Input	integer
--embeddedpixelsize	Consider the unit embedded in metadata, if present	Input	boolean
--unitLength	Enter the metric for unit conversion	Input	string
--pixelsPerunit	Enter the number of pixels per unit of the metric	Input	number
--outDir	Output collection	Output	csvCollection

---

Input type collection is a WIPP's image collection browsable at WIPP web application/Data/Images Collections. Output type csvCollection indicates that result of the successfully run plugin will be available to a user as CSV-files. To access the result in WIPP, navigate to WIPP web application/Workflows, choose the task, expand drop-down list 'Ouputs', and navigate to the URL leading to a WIPP data collection. Input type enum is a single-selection list of options. Input type array is a multi-selection list of options. Input types boolean is represented with a check-box in WIPP's user interface. There are 2 parameters referring input type string - the file pattern applied to image file names in collections defined by parameters intDir and segDir, and the name of the measurement unit defined by optional parameters embeddedpixelsize and pixelsPerunit. The file pattern parameter is mandatory. Its valid values are regular expression style wildcards to filter file names, for example, .* to select all the files or .*c1\.ome\.tif to select just files ending in "c1.ome.tif". Input type integer is a positive integer value or zero. Input type number used in parameter pixelsPerunit is a positive real value defining the number of pixels in the measurement unit defined by parameter unitLength.

Parameter features defines a set of desired features to be calculated. Valid string literal to feature correspondence is listed in the feature table above.

The following command line is an example of running the dockerized feature extractor (image hash 87f3b560bbf2) with only intensity features selected:

docker run -it --mount type=bind,source=/images/collections,target=/data 87f3b560bbf2 --intDir=/data/c1/int --segDir=/data/c1/seg --outDir=/data/output --filePattern=.* --csvfile=separatecsv --features=entropy,kurtosis,skewness,max_intensity,mean_intensity,min_intensity,median,mode,standard_deviation

or its undockerized equivalent:

python main.py --intDir=/images/collections/c1/int --segDir=/images/collections/c1/seg --outDir=/temp_fe/output --filePattern=.* --csvfile=separatecsv --features=entropy,kurtosis,skewness,max_intensity,mean_intensity,min_intensity,median,mode,standard_deviation