nyxus 0.5.0

Scalable library for calculating features from intensity-label image data

Nyxus

A scalable library for calculating features from intensity-label image data

Overview

Nyxus is a feature-rich, highly optimized, Python/C++ application capable of analyzing images of arbitrary size and assembling complex regions of interest (ROIs) split across multiple image tiles and files. This accomplished through multi-threaded tile prefetching and a three phase analysis pipeline shown below.

Nyxus can be used via Python or command line and is available in containerized form for reproducible execution. Nyxus computes over 450 combined intensity, texture, and morphological features at the ROI or whole image level with more in development. Key features that make Nyxus unique among other image feature extraction applications is its ability to operate at any scale, its highly validated algorithms, and its modular nature that makes the addition of new features straightforward.

The docs can be found at Read the Docs.

Getting started

For use in python, the latest version of Nyxus can be installed via the Pip package manager or Conda package manager:

pip install nyxus

or

conda install nyxus -c conda-forge

Usage is very straightforward. Given intensities and labels folders, Nyxus pairs up intensity-label pairs and extracts features from all of them. A summary of the avaialble feature are listed below.

from nyxus import Nyxus
nyx = Nyxus(["*ALL*"])
intensityDir = "/path/to/images/intensities/"
maskDir = "/path/to/images/labels/"
features = nyx.featurize_directory (intensityDir, maskDir)

Alternatively, Nyxus can process explicitly defined pairs of intensity-mask images, for example image "i1" with mask "m1" and image "i2" with mask "m2":

from nyxus import Nyxus
nyx = Nyxus(["*ALL*"])
features = nyx.featurize_files(
    [
        "/path/to/images/intensities/i1.ome.tif", 
        "/path/to/images/intensities/i2.ome.tif"
    ], 
    [
        "/path/to/images/labels/m1.ome.tif", 
        "/path/to/images/labels/m2.ome.tif"
    ])

The features variable is a Pandas dataframe similar to what is shown below.

	mask_image	intensity_image	label	MEAN	MEDIAN	...	GABOR_6
0	p1_y2_r51_c0.ome.tif	p1_y2_r51_c0.ome.tif	1	45366.9	46887	...	0.873016
1	p1_y2_r51_c0.ome.tif	p1_y2_r51_c0.ome.tif	2	27122.8	27124.5	...	1.000000
2	p1_y2_r51_c0.ome.tif	p1_y2_r51_c0.ome.tif	3	34777.4	33659	...	0.942857
3	p1_y2_r51_c0.ome.tif	p1_y2_r51_c0.ome.tif	4	35808.2	36924	...	0.824074
4	p1_y2_r51_c0.ome.tif	p1_y2_r51_c0.ome.tif	5	36739.7	37798	...	0.854067
...	...	...	...	...	...	...	...
734	p5_y0_r51_c0.ome.tif	p5_y0_r51_c0.ome.tif	223	54573.3	54573.3	...	0.980769

Nyxus can also process intensity-mask pairs that are loaded as Numpy arrays using the featurize method. This method takes in either a single pair of 2D intensity-mask pairs or a pair of 3D arrays containing 2D intensity and mask images. There is also two optional parameters to supply names to the resulting dataframe, .

from nyxus import Nyxus
import numpy as np


nyx = Nyxus(["*ALL*"])

intens = [
    [[1, 4, 4, 1, 1],
        [1, 4, 6, 1, 1],
        [4, 1, 6, 4, 1],
        [4, 4, 6, 4, 1]],
                   
    [[1, 4, 4, 1, 1],
    [1, 1, 6, 1, 1],
    [1, 1, 3, 1, 1],
    [4, 4, 6, 1, 1]]
]

seg = [
    [[1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1]],
                
    [[1, 1, 1, 1, 1],
    [1, 1, 1, 1, 1],
    [0, 1, 1, 1, 1],
    [1, 1, 1, 1, 1]]
]


features = nyx.featurize(intens, seg)

The features variable is a Pandas dataframe similar to what is shown below.

	mask_image	intensity_image	label	MEAN	MEDIAN	...	GABOR_6
0	Segmentation1	Intensity1	1	45366.9	46887	...	0.873016
1	Segmentation1	Intensity1	2	27122.8	27124.5	...	1.000000
2	Segmentation1	Intensity1	3	34777.4	33659	...	0.942857
3	Segmentation1	Intensity1	4	35808.2	36924	...	0.824074
...	...	...	...	...	...	...	...
14	Segmentation2	Intensity2	6	54573.3	54573.3	...	0.980769

Note that in this case, default names were provided for the mask_image and intensity_image columns. To supply names for these columns, the optional arguments intensity_names and label_names are used by passing lists of names in. The length of the lists must be the same as the length of the mask and intensity arrays. To name the images, use

intens_names = ['custom_intens_name1', 'custom_intens_name2']
seg_names = ['custom_seg_name1', 'custom_seg_name2']

features = nyx.featurize(intens, seg, intens_name, seg_name)

The features variable will now use the custom names, as shown below

	mask_image	intensity_image	label	MEAN	MEDIAN	...	GABOR_6
0	custom_seg_name1	custom_intens_name1	1	45366.9	46887	...	0.873016
1	custom_seg_name1	custom_intens_name1	2	27122.8	27124.5	...	1.000000
2	custom_seg_name1	custom_intens_name1	3	34777.4	33659	...	0.942857
3	custom_seg_name1	custom_intens_name1	4	35808.2	36924	...	0.824074
...	...	...	...	...	...	...	...
14	custom_seg_name2	custom_intens_name2	6	54573.3	54573.3	...	0.980769

For more information on all of the available options and features, check out the documentation.

Nyxus can also be built from source and used from the command line, or via a pre-built Docker container.

Available features

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 the circle having circumference equal to the ROI's perimeter
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_easy*	All the features except the most time-consuming GABOR, GLCM, and the group of 2D moment features
*all*	All the features

Command line usage

Assuming you built the Nyxus binary as outlined below, the following parameters are available for the command line interface:

Parameter	Description	Type
--csvFile	Save csv file as one csv file for all the images or separate csv file for each image. Acceptable values: 'separatecsv' and 'singlecsv'. Default value: '--csvFile=separatecsv'	string constant
--features	String constant or comma-seperated list of constants requesting a group of features or particular feature. Default value: '--features=*ALL*'	string
--filePattern	Regular expression to match image files in directories specified by parameters '--intDir' and '--segDir'. To match all the files, use '--filePattern=.*'	string
--intDir	Directory of intensity image collection	path
--outDir	Output directory	path
--segDir	Directory of labeled image collection	path
--coarseGrayDepth	(optional) Custom number of grayscale level bins used in texture features. Default: '--coarseGrayDepth=256'	integer
--glcmAngles	(optional) Enabled direction angles of the GLCM feature. Superset of values: 0, 45, 90, and 135. Default: '--glcmAngles=0,45,90,135'	list of integer constants
--intSegMapDir	(optional) Data collection of the ad-hoc intensity-to-mask file mapping. Must be used in combination with parameter '--intSegMapFile'	path
--intSegMapFile	(optional) 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. Must be used in combination with parameter '--intSegMapDir'	string
--pixelDistance	(optional) Number of pixels to treat ROIs within specified distance as neighbors. Default value: '--pixelDistance=5'	integer
--pixelsPerCentimeter	(optional) Number of pixels in centimeter used by unit length-related features. Default value: 0	real
--ramLimit	(optional) Amount of memory not to exceed by Nyxus, in megabytes. Default value: 50% of available memory. Example: '--ramLimit=2000' to use 2,000 megabytes	integer
--reduceThreads	(optional) Number of CPU threads used on the feature calculation step. Default: '--reduceThreads=1'	integer
--skiproi	(optional) Skip ROIs having specified labels. Example: '--skiproi=image1.tif:2,3,4;image2.tif:45,56'	string
--tempDir	(optional) Directory used by temporary out-of-RAM objects. Default value: system temporary directory	path

---

Example: Running Nyxus to process images of specific image channel

Suppose we need to process intensity/mask images of channel 1 :

./nyxus --features=*all_intensity*,*basic_morphology* --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output --filePattern=.*_c1\.ome\.tif --csvFile=singlecsv 

Example: Running Nyxus to process specific image

Suppose we need to process intensity/mask file p1_y2_r68_c1.ome.tif :

./nyxus --features=*all_intensity*,*basic_morphology* --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output --filePattern=p1_y2_r68_c1\.ome\.tif --csvFile=singlecsv 

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

./nyxus --features=*all_intensity*,*basic_morphology* --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output --filePattern=.* --csvFile=singlecsv 

Example: Skipping specified ROIs while extracting features

Suppose we need to blacklist ROI labels 15, 16, and 17 from feature extraction :

./nyxus --skiproi=15,16,17 --features=*all* --intDir=/path/to/intensity/images --segDir=/path/to/mask/images --outDir=/path/to/output --filePattern=.* --csvFile=singlecsv 

Nested features

A separate command line executable "nyxushie" for the hierarchical ROI analysis by finding nested ROIs and aggregating features of child ROIs within corresponding parent features is available. Its command line format is:

nyxushie <segment image collection dir> <file pattern> <channel signature> <parent channel> <child channel> <features dir> [-aggregate=<aggregation method>]

where

<segment image collection dir> is directory of the segment images collection ;

<file pattern> is a regular expression to filter files in <segment image collection dir> ;

<channel signature> is a signature of the channel part in an image file name ;

<parent channel> is an integer channel number where parent ROIs are expected ;

<child channel> is an integer channel number where child ROIs are expected ;

<features dir> is a directory used as the output of parent-child ROI relations and, if aggregation is requested, where CSV files of Nyxus features produced with Nyxus command line option --csvfile=separatecsv is located ;

(optional) <aggregation method> is a method instructing how to aggregate child ROI features under a parent ROI.

Valid aggregation method options are: SUM, MEAN, MIN, MAX, or WMA (weighted mean average).

Example: we need to process collection of mask images located in directory "~/data/image-collection1/seg" considering only images named "train_.*\.tif" whose channel information begins with characters "_ch" (_ch0, _ch1, etc.) telling Nyxushie to treat channel 1 images as source of parent ROIs and channel 0 images as source of child ROIs. The output directory needs to be "~/results/result1". The command line will be

nyxushie ~/data/image-collection1/seg train_.*\\.tif _ch 1 0 ~/results/result1

Nested features Python API

The nested features functionality can also be utilized in Python using the Nested class in nyxus. The Nested class contains two methods, find_relations and featurize.

The find_relations method takes in a path to the label files, along with a child filepattern to identify the files in the child channel and a parent filepattern to match the files in the parent channel. The find_relation method returns a Pandas DataFrame containing a mapping between parent ROIs and the respective child ROIs.

The featurize method takes in the parent-child mapping along with the features of the ROIs in the child channel. If a list of aggregate functions is provided to the constructor, this method will return a pivoted DataFrame where the rows are the ROI labels and the columns are grouped by the features.

Example: Using aggregate functions

from nyxus import Nyxus, Nested
import numpy as np

int_path = 'path/to/intensity'
seg_path = 'path/to/segmentation'

nyx = Nyxus(['GABOR'])

child_features = nyx.featurize(int_path, seg_path, file_pattern='p[0-9]_y[0-9]_r[0-9]_c0\.ome\.tif')

nest = Nested(['sum', 'mean', 'min', ('nanmean', lambda x: np.nanmean(x))])

df = nest.find_relations(seg_path, 'p{r}_y{c}_r{z}_c1.ome.tif', 'p{r}_y{c}_r{z}_c0.ome.tif')

df2 = nest.featurize(df, features)

The parent-child map is

    Image              Parent_Label  Child_Label
    0  /path/to/image          72             65
    1  /path/to/image          71             66
    2  /path/to/image          70             64
    3  /path/to/image          68             61
    4  /path/to/image          67             65

and the aggregated DataFrame is

            GABOR_0                                  GABOR_1                                  GABOR_2              ... 
            sum        mean      min       nanmean    sum      mean       min       nanmean   sum      mean        ...
    label                                                                                                          ...                                                                                                      
     1      24.010227  0.666951  0.000000  0.666951  19.096262  0.530452  0.001645  0.530452  17.037345  0.473260  ... 
     2      13.374170  0.445806  0.087339  0.445806   7.279187  0.242640  0.075000  0.242640   6.390529  0.213018  ...  
     3       5.941783  0.198059  0.000000  0.198059   3.364149  0.112138  0.000000  0.112138   2.426409  0.080880  ...  
     4      13.428773  0.559532  0.000000  0.559532  12.021938  0.500914  0.008772  0.500914   9.938915  0.414121  ...  
     5       6.535722  0.181548  0.000000  0.181548   1.833463  0.050930  0.000000  0.050930   2.083023  0.057862  ...

Example: Without aggregate functions

from nyxus import Nyxus, Nested
import numpy as np

int_path = 'path/to/intensity'
seg_path = 'path/to/segmentation'

nyx = Nyxus(['GABOR'])

child_features = nyx.featurize(int_path, seg_path, file_pattern='p[0-9]_y[0-9]_r[0-9]_c0\.ome\.tif')

nest = Nested()

df = nest.find_relations(seg_path, 'p{r}_y{c}_r{z}_c1.ome.tif', 'p{r}_y{c}_r{z}_c0.ome.tif')

df2 = nest.featurize(df, features)

the parent-child map remains the same but the featurize result becomes

                     GABOR_0                                                                ...    
    Child_Label       1          2         3         4         5    6    7    8    9    10  ...    
    label                                                                                   ...
    1            0.666951       NaN       NaN       NaN       NaN  NaN  NaN  NaN  NaN  NaN  ...     
    2                 NaN  0.445806       NaN       NaN       NaN  NaN  NaN  NaN  NaN  NaN  ...     
    3                 NaN       NaN  0.198059       NaN       NaN  NaN  NaN  NaN  NaN  NaN  ...     
    4                 NaN       NaN       NaN  0.559532       NaN  NaN  NaN  NaN  NaN  NaN  ...     
    5                 NaN       NaN       NaN       NaN  0.181548  NaN  NaN  NaN  NaN  NaN  ...

Building from source

Nyxus can either be build inside a conda environment or independently outside of it. For the later case, we provide a script to make it easier to download and build all the necessary dependencies.

Inside Conda

Nyxus uses a CMake build system. To build the command line interface, pass -DBUILD_CLI=ON in the cmake command. For building with GPU support, use -DUSEGPU=ON flag in the cmake command. Here are the few notes on building with GPU support.

• Currently, GPU builds on Mac OS is not supported.
• Due to the limitation of CUDA Development toolkit, upto GCC 9.X versions can be used on Linux.
• On Windows, we assume the correct version of CUDA toolkit and compiler is installed that is compatible with the Microsoft Visual Studio C++ compiler.

Below is an example of how to build Nyxus inside a conda environment on Linux.

git clone https://github.com/PolusAI/nyxus.git
cd nyxus
conda install -y -c conda-forge --file ci-utils/envs/conda_cpp.txt --file ci-utils/envs/conda_linux_compiler.txt --file ci-utils/envs/conda_py.txt --file ci-utils/envs/conda_linux_gpu.txt
mkdir build
cd build
cmake -DBUILD_CLI=ON -DUSEGPU=ON -DCMAKE_PREFIX_PATH=$CONDA_PREFIX -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX ..
make -j4

If you are building on Mac or Windows, skip the dependencies from ci-utils/envs/conda_linux_compiler.txt and ci-utils/envs/conda_linux_gpu.txt

To install the python package in the conda environment on Linux, use the following direction.

git clone https://github.com/PolusAI/nyxus.git
cd nyxus
conda install -y -c conda-forge --file ci-utils/envs/conda_cpp.txt --file ci-utils/envs/conda_linux_compiler.txt --file ci-utils/envs/conda_linux_gpu.txt --file ci-utils/envs/conda_py.txt
CMAKE_ARGS=" -DBUILD_CLI=ON -DUSEGPU=ON -DCMAKE_PREFIX_PATH=$CONDA_PREFIX -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX " python setup.py install

We also provide an example script that downloads conda, installs the necessary dependencies and then builds both the CLI and the python library on Linux. To run the script, do the following.

git clone https://github.com/PolusAI/nyxus.git
cd nyxus/ci-utils
./build_conda.sh ..

Without Using Conda

To build Nyxus outside of a conda environment, use the following example.

git clone https://github.com/PolusAI/nyxus.git
cd nyxus
mkdir build
cd build
bash ../ci-utils/install_prereq_linux.sh
cmake -DBUILD_CLI=ON -DUSEGPU=ON -DCMAKE_PREFIX_PATH=./local_install -DCMAKE_INSTALL_PREFIX=./local_install ..
make -j4

Running via Docker

Running Nyxus from a local directory freshly made Docker container is a good idea. It allows one to test-run conteinerized Nyxus before it reaches Docker cloud deployment.

To search available Nyxus images run command

docker search nyxus

and you'll be shown that it's available at least via organization 'polusai'. To pull it, run

docker pull polusai/nyxus

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

docker run -it [--gpus all] --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

Install from sources and package into a Docker image

If you want to build your own Nyxus Docker container we provide a convenient shell script:

./ci-utils/build-docker.sh

Dependencies

Nyxus is tested with Python 3.6+. Nyxus relies on the the following packages:

pybind11 >= 2.8.1
libTIFF >= 3.6.1
Z5 >=2.0.15
Each of these dependencies also have hierarchical dependencies and so we recommend using the conda build system when building from source.

WIPP Usage

Nyxus is available as plugin for WIPP.

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.