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Merge pull request #169 from kostrykin/dev/filter
Add `ridge_filter_skimage` tool for ridge filtering
2 parents a6fd77b + 4dcbf29 commit 85b0f6a

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‎tools/ridge_filter/.shed.yml‎

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categories:
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- Imaging
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description: Ridge filter for images
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long_description: Apply a ridge filter to an image
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name: ridge_filter
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owner: imgteam
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homepage_url: https://scikit-image.org/docs/0.25.x/auto_examples/edges/plot_ridge_filter.html
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remote_repository_url: https://github.com/BMCV/galaxy-image-analysis/tree/master/tools/ridge_filter/

‎tools/ridge_filter/creators.xml‎

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../../macros/creators.xml

‎tools/ridge_filter/filter_skimage.py‎

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import argparse
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import json
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from typing import (
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Any,
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Callable,
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)
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import giatools
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import numpy as np
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import skimage.filters
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filters = {
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'frangi': lambda img, **kwargs: (
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apply_nd_filter(skimage.filters.frangi, img, **kwargs)
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),
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'hessian': lambda img, **kwargs: (
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apply_nd_filter(skimage.filters.hessian, img, **kwargs)
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),
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'laplace': lambda img, **kwargs: (
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apply_nd_filter(skimage.filters.laplace, img, **kwargs)
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),
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'meijering': lambda img, **kwargs: (
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apply_nd_filter(skimage.filters.meijering, img, **kwargs)
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),
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'sato': lambda img, **kwargs: (
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apply_nd_filter(skimage.filters.sato, img, **kwargs)
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),
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}
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def apply_nd_filter(
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filter_impl: Callable[[np.ndarray, Any, ...], np.ndarray],
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img: giatools.Image,
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dtype: str,
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**kwargs: Any,
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) -> giatools.Image:
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"""
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Apply the filter to the 2-D/3-D, potentially multi-frame and multi-channel image.
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"""
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result_data = np.empty(img.data.shape, dtype=dtype)
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for qtc in np.ndindex(
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img.data.shape[ 0], # Q axis
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img.data.shape[ 1], # T axis
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img.data.shape[-1], # C axis
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):
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sl = np.s_[*qtc[:2], ..., qtc[2]] # noqa: E999
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arr = img.data[sl]
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assert arr.ndim == 3 # sanity check, should always be True
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# Perform 2-D or 3-D filtering
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if arr.shape[0] == 1:
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info = 'Performing 2-D filtering'
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result_data[sl][0] = filter_impl(arr[0], **kwargs).astype(dtype)
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else:
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info = 'Performing 3-D filtering'
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result_data[sl] = filter_impl(arr, **kwargs).astype(dtype)
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# Print status info
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print(info)
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# Return results as 16bit, 32bit, or 64bit floating point
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return giatools.Image(result_data.astype(dtype), img.axes)
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def apply_filter(
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input_filepath: str,
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output_filepath: str,
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filter_type: str,
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**kwargs: Any,
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):
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# Validate and transform input parameters
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params = dict(kwargs)
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if (sigma_min := params.pop('sigma_min', None)) is not None and (sigma_max := params.pop('sigma_max', None)) is not None:
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num_sigma = params.pop('num_sigma')
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if sigma_min < sigma_max:
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params['sigmas'] = np.linspace(sigma_min, sigma_max, num_sigma)
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elif sigma_min == sigma_max:
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params['sigmas'] = [sigma_min]
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else:
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raise ValueError(f'Minimum sigma ({sigma_min:g}) must not be greater than Maximum sigma ({sigma_max:g})')
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# Read the input image
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img = giatools.Image.read(input_filepath)
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# Perform filtering
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print(f'Applying filter: "{filter_type}"')
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filter_impl = filters[filter_type]
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res = filter_impl(img, **params).normalize_axes_like(img.original_axes)
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# Adopt metadata and write the result
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res.metadata = img.metadata
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res.write(output_filepath, backend='tifffile')
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('input', type=str)
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parser.add_argument('output', type=str)
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parser.add_argument('params', type=str)
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args = parser.parse_args()
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# Read the config file
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with open(args.params) as cfgf:
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cfg = json.load(cfgf)
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apply_filter(
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args.input,
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args.output,
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**cfg,
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)

‎tools/ridge_filter/skimage.xml‎

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<tool id="ridge_filter_skimage" name="Apply ridge filter" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.05">
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<description>with scikit-image</description>
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<macros>
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<import>creators.xml</import>
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<import>tests.xml</import>
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<token name="@TOOL_VERSION@">0.22.0</token>
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<token name="@VERSION_SUFFIX@">1</token>
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<xml name="base_params">
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<param name="black_ridges" type="select" label="Mode of operation">
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<option value="true" selected="true">Enhance dark ridges (low image intensities)</option>
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<option value="false">Enhance bright ridges (high image intensities)</option>
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</param>
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<param name="sigma_min" type="float" value="1" min="0.1" label="Minimum sigma" help="Lower bound of the scale for multi-scale analysis."/>
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<param name="sigma_max" type="float" value="3" min="0.1" label="Maximum sigma" help="Upper bound of the scale for multi-scale analysis."/>
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<param name="num_sigma" type="integer" value="10" min="2" label="Number of sigma steps for multi-scale analysis"/>
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</xml>
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<xml name="frangi_params">
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<expand macro="base_params"/>
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<param name="alpha" type="float" min="0" value="0.5" label="Alpha"
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help="Frangi correction constant that adjusts the filter's sensitivity to deviation from a plate-like structure."/>
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<param name="beta" type="float" min="0" value="0.5" label="Beta"
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help="Frangi correction constant that adjusts the filter's sensitivity to deviation from a blob-like structure."/>
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</xml>
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</macros>
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<creator>
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<expand macro="creators/bmcv"/>
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<expand macro="creators/kostrykin"/>
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</creator>
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<edam_operations>
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<edam_operation>operation_3443</edam_operation>
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</edam_operations>
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<xrefs>
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<xref type="bio.tools">galaxy_image_analysis</xref>
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<xref type="bio.tools">scikit-image</xref>
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<xref type="biii">scikit-image</xref>
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</xrefs>
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<requirements>
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<requirement type="package" version="@TOOL_VERSION@">scikit-image</requirement>
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<requirement type="package" version="0.5.2">giatools</requirement>
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</requirements>
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<command detect_errors="aggressive"><![CDATA[
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python '$__tool_directory__/filter_skimage.py'
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'$input'
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'$output'
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'$params'
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]]></command>
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<configfiles>
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<configfile name="params"><![CDATA[
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{
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#if $filter.filter_type == "frangi" or $filter.filter_type == "hessian" or $filter.filter_type == "meijering" or $filter.filter_type == "sato"
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"black_ridges": $filter.black_ridges,
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"sigma_min": $filter.sigma_min,
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"sigma_max": $filter.sigma_max,
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"num_sigma": $filter.num_sigma,
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#end if
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#if $filter.filter_type == "frangi" or $filter.filter_type == "hessian"
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"alpha": $filter.alpha,
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"beta": $filter.beta,
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#end if
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#if $filter.filter_type == "laplace"
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"ksize": $filter.ksize,
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#end if
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"dtype": "$dtype",
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"filter_type": "$filter.filter_type"
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}
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]]></configfile>
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</configfiles>
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<inputs>
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<param name="input" type="data" format="tiff,png" label="Input image"/>
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<param name="dtype" type="select" label="Output pixel type"
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help="Data type used to store the pixel values in the output image.">
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<option value="float64" selected="True">64-bit floating point</option>
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<option value="float32">32-bit floating point</option>
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<option value="float16">16-bit floating point</option>
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</param>
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<conditional name="filter">
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<param name="filter_type" type="select" label="Filter">
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<option value="frangi" selected="True">Frangi vesselness filter</option>
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<option value="hessian">Hybrid Hessian filter</option>
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<option value="laplace">Laplace filter</option>
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<option value="meijering">Meijering neuriteness filter</option>
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<option value="sato">Sato tubeness filter</option>
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</param>
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<when value="frangi">
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<expand macro="frangi_params"/>
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</when>
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<when value="hessian">
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<expand macro="frangi_params"/>
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</when>
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<when value="laplace">
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<param name="ksize" type="integer" value="3" label="Kernel size"
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help="Define the size of the discrete Laplacian operator such that it will have the size of a hypercube with this edge length (in pixels)."/>
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</when>
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<when value="meijering">
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<expand macro="base_params"/>
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</when>
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<when value="sato">
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<expand macro="base_params"/>
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</when>
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</conditional>
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</inputs>
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<outputs>
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<data format="tiff" name="output"/>
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</outputs>
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<tests>
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<!-- Single-image (Q), Single-frame (T), Single-slice (Z), multi-channel (C), Frangi (dark) -->
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<test>
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<param name="input" value="scikit-image/retina.png"/>
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<param name="dtype" value="float16"/>
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<conditional name="filter">
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<param name="filter_type" value="frangi"/>
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<param name="black_ridges" value="true"/>
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<param name="sigma_min" value="0.5"/>
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<param name="sigma_max" value="20"/>
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<param name="num_sigma" value="10"/>
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<param name="alpha" value="0.5"/>
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<param name="beta" value="0.5"/>
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</conditional>
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<expand macro="tests/intensity_image_diff" name="output" value="retina_frangi.tiff" ftype="tiff"/>
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<assert_stdout>
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<has_line line='Applying filter: "frangi"'/>
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<has_line line="Performing 2-D filtering"/>
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</assert_stdout>
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</test>
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<!-- Single-image (Q), Single-frame (T), Single-slice (Z), single-channel (C), Hessian (dark) -->
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<test>
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<param name="input" value="retina_0c_0z_0t_0q.tiff"/>
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<param name="dtype" value="float16"/>
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<conditional name="filter">
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<param name="filter_type" value="hessian"/>
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<param name="black_ridges" value="true"/>
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<param name="sigma_min" value="0.5"/>
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<param name="sigma_max" value="20"/>
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<param name="num_sigma" value="10"/>
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<param name="alpha" value="0.5"/>
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<param name="beta" value="0.5"/>
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</conditional>
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<expand macro="tests/intensity_image_diff" name="output" value="retina_0c_0z_0t_0q_hessian.tiff" ftype="tiff"/>
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<assert_stdout>
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<has_line line='Applying filter: "hessian"'/>
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<has_line line="Performing 2-D filtering"/>
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</assert_stdout>
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</test>
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<!-- Single-image (Q), Multi-frame (T), Single-slice (Z), single-channel (C), Meijering (dark) -->
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<test>
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<param name="input" value="retina_0c_0z_3t_0q.tiff"/>
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<param name="dtype" value="float16"/>
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<conditional name="filter">
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<param name="filter_type" value="meijering"/>
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<param name="black_ridges" value="true"/>
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<param name="sigma_min" value="0.5"/>
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<param name="sigma_max" value="20"/>
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<param name="num_sigma" value="10"/>
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</conditional>
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<expand macro="tests/intensity_image_diff" name="output" value="retina_0c_0z_3t_0q_meijering.tiff" ftype="tiff"/>
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<assert_stdout>
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<has_line line='Applying filter: "meijering"'/>
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<has_line line="Performing 2-D filtering"/>
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</assert_stdout>
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</test>
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<!-- Multi-image (Q), Single-frame (T), Single-slice (Z), single-channel (C), Sato (dark) -->
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<test>
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<param name="input" value="retina_0c_0z_0t_2q.tiff"/>
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<param name="dtype" value="float16"/>
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<conditional name="filter">
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<param name="filter_type" value="sato"/>
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<param name="black_ridges" value="true"/>
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<param name="sigma_min" value="0.5"/>
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<param name="sigma_max" value="20"/>
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<param name="num_sigma" value="10"/>
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</conditional>
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<expand macro="tests/intensity_image_diff" name="output" value="retina_0c_0z_0t_2q_sato.tiff" ftype="tiff"/>
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<assert_stdout>
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<has_line line='Applying filter: "sato"'/>
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<has_line line="Performing 2-D filtering"/>
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</assert_stdout>
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</test>
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<!-- Single-image (Q), Single-frame (T), Multi-slice (Z), single-channel (C), Laplace -->
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<test>
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<param name="input" value="retina_0c_5z_0t_0q.tiff"/>
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<param name="dtype" value="float16"/>
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<conditional name="filter">
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<param name="filter_type" value="laplace"/>
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<param name="ksize" value="5"/>
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</conditional>
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<expand macro="tests/intensity_image_diff" name="output" value="retina_0c_5z_0t_0q_laplace.tiff" ftype="tiff"/>
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<assert_stdout>
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<has_line line='Applying filter: "laplace"'/>
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<has_line line="Performing 3-D filtering"/>
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</assert_stdout>
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</test>
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<!-- Bright ridges, Single-image (Q), Single-frame (T), Multi-slice (Z), single-channel (C), Frangi (bright) -->
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<test>
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<param name="input" value="retina_inv_0c_0z_0t_0q.tiff"/>
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<param name="dtype" value="float16"/>
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<conditional name="filter">
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<param name="filter_type" value="frangi"/>
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<param name="black_ridges" value="false"/>
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<param name="sigma_min" value="0.5"/>
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<param name="sigma_max" value="20"/>
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<param name="num_sigma" value="10"/>
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<param name="alpha" value="0.5"/>
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<param name="beta" value="0.5"/>
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</conditional>
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<expand macro="tests/intensity_image_diff" name="output" value="retina_inv_0c_0z_0t_0q_frangi.tiff" ftype="tiff"/>
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<assert_stdout>
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<has_line line='Applying filter: "frangi"'/>
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<has_line line="Performing 2-D filtering"/>
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</assert_stdout>
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</test>
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</tests>
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<help>
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**Applies a ridge filter to an image.**
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Ridge filters can be used to detect ridge-like structures, such as neurites, tubes, vessels, wrinkles, or rivers. Different
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ridge filters may be suited for detecting different structures, e.g., depending on contrast or noise level. 2-D and 3-D
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images are supported. For multi-channel images, the filter is applied to all channels of the image. For time-series images,
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the filter is also applied for all time steps.
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Frangi vesselness filter
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========================
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Filter an image with the Frangi vesselness filter. This filter can be used to detect continuous ridges, e.g. vessels,
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wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects. Calculates the
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eigenvalues of the Hessian to compute the similarity of an image region to vessels, according to the method described in
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Frangi et al. (1998).
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Hybrid Hessian filter
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=====================
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Filter an image with the Hybrid Hessian filter (Schrijver 2001, Kroon 2009). This filter can be used to detect continuous
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edges, e.g. vessels, wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects.
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Almost equal to Frangi filter, but uses alternative method of smoothing.
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Laplace filter
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==============
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Find the edges of an image using the Laplace operator.
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Meijering neuriteness filter
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============================
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Filter an image with the Meijering neuriteness filter. This filter can be used to detect continuous ridges, e.g. neurites,
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wrinkles, rivers. It can be used to calculate the fraction of the whole image containing such objects. Calculates the
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eigenvalues of the Hessian to compute the similarity of an image region to neurites, according to the method described in
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Meijering et al. (2004).
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Sato tubeness filter
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====================
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Filter an image with the Sato tubeness filter. This filter can be used to detect continuous ridges, e.g. tubes, wrinkles,
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rivers. It can be used to calculate the fraction of the whole image containing such objects. Calculates the eigenvalues of
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the Hessian to compute the similarity of an image region to tubes, according to the method described in Sato et al. (1998).
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</help>
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<citations>
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<citation type="doi">10.1007/BFb0056195</citation>
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<citation type="doi">10.1002/cyto.a.20022</citation>
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<citation type="doi">10.1016/S1361-8415(98)80009-1</citation>
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</citations>
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</tool>

‎tools/ridge_filter/test-data/retina_0c_0z_0t_0q.tiff‎

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‎tools/ridge_filter/test-data/retina_0c_0z_0t_0q_hessian.tiff‎

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‎tools/ridge_filter/test-data/retina_0c_0z_0t_2q.tiff‎

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‎tools/ridge_filter/test-data/retina_0c_0z_0t_2q_sato.tiff‎

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‎tools/ridge_filter/test-data/retina_0c_0z_3t_0q.tiff‎

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‎tools/ridge_filter/test-data/retina_0c_0z_3t_0q_meijering.tiff‎

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