diff --git a/cli/collageradiomicscli.py b/cli/collageradiomicscli.py new file mode 100644 index 0000000..e57b4be --- /dev/null +++ b/cli/collageradiomicscli.py @@ -0,0 +1,93 @@ +#!usr/bin/env python +# -*- coding: utf-8 -*- + +import click +import os +import sys +import SimpleITK as sitk +import csv +from scipy import stats +import numpy as np + +import collageradiomics + +@click.command() +@click.option('-i', '--input', required=True, help='Path to an input image from which features will be extracted.') +@click.option('-m', '--mask', required=True, help='Path to a mask that will be considered as binary. The highest pixel value will be considered as information and all other values will be considered outside the mask') +@click.option('-o', '--outputfile', required=True, help='Path to the output CSV file.') +@click.option('-v', '--verbose', default=True, help='Provides additional debug output.') +@click.option('-d', '--dimensions', help='Optional number of dimensions upon which to run collage. Supported values are 2 and 3. If left out, we will default to the dimensionality of the image itself, which may not reflect expected behavior if the image has an alpha channel.', type=click.IntRange(2, 3, clamp=True)) +@click.option('-s', '--svdradius', default=5, help='SVD radius is used for the dominant angle calculation pixel radius. DEFAULTS to 5 and is suggested to remain at the default.') +@click.option('-h', '--haralickwindow', default=-1, help='Number of pixels around each pixel used to calculate the haralick texture. DEFAULTS to svdradius * 2 - 1.') +@click.option('-b', '--binsize', default=64, help='Number of bins to use while calculating the grey level cooccurence matrix. DEFAULTS to 64.') +def run(input, mask, outputfile, verbose, dimensions, svdradius, haralickwindow, binsize): + """CoLlAGe captures subtle anisotropic differences in disease pathologies by measuring entropy of co-occurrences of voxel-level gradient orientations on imaging computed within a local neighborhood.""" + + image = sitk.ReadImage(input) + mask = sitk.ReadImage(mask) + + image_array = sitk.GetArrayFromImage(image) + mask_array = sitk.GetArrayFromImage(mask) + + # Remove any extra array dimensions if the user explicitly asks for 2D. + if dimensions == 2: + image_array = image_array[:,:,0] + mask_array = mask_array [:,:,0] + + collage = collageradiomics.Collage( + image_array, + mask_array, + svd_radius=svdradius, + verbose_logging=verbose, + num_unique_angles=binsize) + + collage.execute() + + # Create a csv file at the passed in output file location. + with open(outputfile, 'w', newline='') as csv_output_file: + writer = csv.writer(csv_output_file) + + # Write the columns. + writer.writerow(['FeatureName', 'Value']) + for feature in collageradiomics.HaralickFeature: + feature_output = collage.get_single_feature_output(feature) + if image_array.ndim == 2: + feature_output = feature_output[~np.isnan(feature_output)] + + # NumPy supports median natively, we'll use that. + median = np.nanmedian(feature_output, axis=None) + + # Use SciPy for kurtosis, variance, and skewness. + feature_stats = stats.describe(feature_output, axis=None) + + # Write CSV row for current feature. + _write_csv_stats_row(writer, feature, median, feature_stats.skewness, feature_stats.kurtosis, feature_stats.variance) + else: + # Extract phi and theta angles. + feature_output_theta = feature_output[:,:,:,0] + feature_output_phi = feature_output[:,:,:,1] + + # Remove NaN for stat calculations. + feature_output_theta = feature_output_theta[~np.isnan(feature_output_theta)] + feature_output_phi = feature_output_phi[~np.isnan(feature_output_phi)] + + # NumPy supports median natively, we'll use that. + median_theta = np.nanmedian(feature_output_theta, axis=None) + median_phi = np.nanmedian(feature_output_phi, axis=None) + + # Use SciPy for kurtosis, variance, and skewness. + feature_stats_theta = stats.describe(feature_output_theta.flatten(), axis=None) + feature_stats_phi = stats.describe(feature_output_phi.flatten(), axis=None) + + # Write CSV rows for each angle. + _write_csv_stats_row(writer, feature, median_theta, feature_stats_theta.skewness, feature_stats_theta.kurtosis, feature_stats_theta.variance, 'Theta') + _write_csv_stats_row(writer, feature, median_phi, feature_stats_phi.skewness, feature_stats_phi.kurtosis, feature_stats_phi.variance, 'Phi') + +def _write_csv_stats_row(writer, feature, median, skewness, kurtosis, variance, suffix=''): + writer.writerow([f'Collage{feature.name}Median{suffix}', f'{median:.10f}']) + writer.writerow([f'Collage{feature.name}Skewness{suffix}', f'{skewness:.10f}']) + writer.writerow([f'Collage{feature.name}Kurtosis{suffix}', f'{kurtosis:.10f}']) + writer.writerow([f'Collage{feature.name}Variance{suffix}', f'{variance:.10f}']) + +if __name__ == '__main__': + run() \ No newline at end of file diff --git a/cli/setup.py b/cli/setup.py new file mode 100644 index 0000000..526bf30 --- /dev/null +++ b/cli/setup.py @@ -0,0 +1,33 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from setuptools import setup + +setup(name='collageradiomicscli', + version='1.0.0', + description='Get Collage features from an image and a binary mask', + url='https://github.com/radxtools/collageradiomics', + python_requires='>=3.6', + author='Toth Technology', + author_email='toth-tech@hillyer.me', + license='BSD-3-Clause', + zip_safe=False, + install_requires=[ + 'collageradiomics', + 'setuptools>=47', + 'SimpleITK==1.2.4', + 'click' + ], + scripts=['collageradiomicscli.py'], + classifiers=[ + 'Intended Audience :: Science/Research', + 'Intended Audience :: Developers', + 'License :: OSI Approved :: BSD License', + 'Operating System :: OS Independent', + 'Operating System :: POSIX :: Linux', + 'Operating System :: Microsoft :: Windows :: Windows 10', + 'Operating System :: MacOS', + 'Programming Language :: Python :: 3', + 'Topic :: Scientific/Engineering :: Bio-Informatics', + ] + )