Napari simpleitk image processing

Robert Haase

Last update: Dec 19, 2022

Related tags

Image Processing napari-plugin

Overview

napari-simpleitk-image-processing (n-SimpleITK)

Process images using SimpleITK in napari

Usage

Filters of this napari plugin can be found in the Tools > Filtering menu. Segmentation algorithms and tools for post-processing segmented (binary or label) images can be found in the Tools > Segmentation menu. All filters implemented in this napari plugin are also demonstrated in this notebook.

Gaussian blur

Applies a Gaussian blur to an image. This might be useful for denoising, e.g. before applying the Threshold-Otsu method.

Median filter

Applies a median filter to an image. Compared to the Gaussian blur this method preserves edges in the image better. It also performs slower.

Bilateral filter

The bilateral filter allows denoising an image while preserving edges.

Threshold Otsu

Binarizes an image using Otsu's method.

Connected Component Labeling

Takes a binary image and labels all objects with individual numbers to produce a label image.

Signed Maurer distance map

A distance map (more precise: Signed Maurer Distance Map) can be useful for visualizing distances within binary images between black/white borders. Positive values in this image correspond to a white (value=1) pixel's distance to the next black pixel. Black pixel's (value=0) distance to the next white pixel are represented in this map with negative values.

Binary fill holes

Fills holes in a binary image.

Touching objects labeling

Starting from a binary image, touching objects can be splits into multiple regions, similar to the Watershed segmentation in ImageJ.

Morphological Watershed

The morhological watershed allows to segment images showing membranes. Before segmentation, a filter such as the Gaussian blur or a median filter should be used to eliminate noise. It also makes sense to increase the thickness of membranes using a maximum filter. See this notebook for details.

Watershed-Otsu-Labeling

This algorithm uses Otsu's thresholding method in combination with Gaussian blur and the Watershed-algorithm approach to label bright objects such as nuclei in an intensity image. The alogrithm has two sigma parameters and a level parameter which allow you to fine-tune where objects should be cut (spot_sigma) and how smooth outlines should be (outline_sigma). The watershed_level parameter determines how deep an intensity valley between two maxima has to be to differentiate the two maxima. This implementation is similar to Voronoi-Otsu-Labeling in clesperanto.

Richardson-Lucy Deconvolution

Richardson-Lucy deconvolution allows to restore image quality if the point-spread-function of the optical system used for acquisition is known or can be approximated.

This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation

You can install napari-simpleitk-image-processing via pip:

pip install napari-simpleitk-image-processing

To install latest development version :

pip install git+https://github.com/haesleinhuepf/napari-simpleitk-image-processing.git

Contributing

Contributions are very welcome. There are many useful algorithms available in SimpleITK. If you want another one available here in this napari plugin, don't hesitate to send a pull-request. This repository just holds wrappers for SimpleITK-functions, see this file for how those wrappers can be written.

License

Distributed under the terms of the BSD-3 license, "napari-simpleitk-image-processing" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.

Comments

Error if no intensity image is provided

The label statistics function produces an error if an intensity image is not provided. This happens even in cases where only statistics that do not require intensity are requested (eg: size, shape, position).

To reproduce:

Open napari and load some labels

import napari
from skimage.data import binary_blobs
from skimage.measure import label

blobs = label(binary_blobs(length=64, volume_fraction=0.05, n_dim=3))
viewer = napari.view_labels(blobs)

Open "label statistics" from the napari-simpleitk-image-processing plugin menu: Plugins > napari-simpleitk-image-processing > label statistics
Uncheck the "intensity" checkbox (but leave the "size" checkbox selected).
Click the "Run" button on the plugin, and observe the error message

Full traceback (click to expand):

  File "C:\Users\CryoEM\.conda\envs\napari-empanada\lib\site-packages\SimpleITK\extra.py", line 184, in _get_sitk_pixelid
    return _np_sitk[numpy_array_type.dtype]
KeyError: dtype('O')

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\CryoEM\.conda\envs\napari-empanada\lib\site-packages\magicgui\widgets\_bases\value_widget.py", line 57, in _on_value_change
    self.changed.emit(value)
  File "psygnal\\_signal.py", line 725, in psygnal._signal.SignalInstance.emit
  File "psygnal\\_signal.py", line 767, in psygnal._signal.SignalInstance._run_emit_loop
  File "psygnal\\_signal.py", line 768, in psygnal._signal.SignalInstance._run_emit_loop
  File "psygnal\\_signal.py", line 788, in psygnal._signal.SignalInstance._run_emit_loop
  File "C:\Users\CryoEM\.conda\envs\napari-empanada\lib\site-packages\magicgui\widgets\_function_gui.py", line 207, in _disable_button_and_call
    self.__call__()
  File "C:\Users\CryoEM\.conda\envs\napari-empanada\lib\site-packages\magicgui\widgets\_function_gui.py", line 318, in __call__
    value = self._function(*bound.args, **bound.kwargs)
  File "C:\Users\CryoEM\.conda\envs\napari-empanada\lib\site-packages\napari_simpleitk_image_processing\_simpleitk_image_processing.py", line 814, in label_statistics
    sitk_intensity_image = sitk.GetImageFromArray(intensity_image)
  File "C:\Users\CryoEM\.conda\envs\napari-empanada\lib\site-packages\SimpleITK\extra.py", line 292, in GetImageFromArray
    id = _get_sitk_pixelid(z)
  File "C:\Users\CryoEM\.conda\envs\napari-empanada\lib\site-packages\SimpleITK\extra.py", line 189, in _get_sitk_pixelid
    raise TypeError('dtype: {0} is not supported.'.format(numpy_array_type.dtype))
TypeError: dtype: object is not supported.

If you suspect this is an IPython 8.4.0 bug, please report it at:
    https://github.com/ipython/ipython/issues
or send an email to the mailing list at [email protected]

You can print a more detailed traceback right now with "%tb", or use "%debug"
to interactively debug it.

Extra-detailed tracebacks for bug-reporting purposes can be enabled via:
    %config Application.verbose_crash=True

Additional details:

Operating system: Windows 10
python version: 3.9
napari version: 0.4.15
napari-simpleitk-image-processing version: 0.4.0

opened by GenevieveBuckley 3

import fails with OSError on headless system

importing napari_simpleitk_image_processing fails with errors about missing GL libraries (see below). I assume this means that this plugin needs a working display?

is there a workaround to make this plugin work without gl support?

Cheers,

Till

`napari_simpleitk_image_processing` import errors
---------------------------------------------------------------------------
OSError Traceback (most recent call last) Input In [6], in <cell line: 7>() 5 import numpy as np 6 #import napari_segment_blobs_and_things_with_membranes as nsbatwm ----> 7 import napari_simpleitk_image_processing as nsitk

File /app/env/lib/python3.9/site-packages/napari_simpleitk_image_processing/init.py:9, in 3 common_alias = "nsitk" 8 from ._function import napari_experimental_provide_function ----> 9 from ._simpleitk_image_processing import
10 plugin_function,
11 median_filter,
12 gaussian_blur,
13 threshold_otsu,
14 threshold_intermodes,
15 threshold_kittler_illingworth,
16 threshold_li,
17 threshold_moments,
18 threshold_renyi_entropy,
19 threshold_shanbhag,
20 threshold_yen,
21 threshold_isodata,
22 threshold_triangle,
23 threshold_huang,
24 threshold_maximum_entropy,
25 signed_maurer_distance_map,
26 morphological_watershed,
27 morphological_gradient,
28 standard_deviation_filter,
29 simple_linear_iterative_clustering,
30 scalar_image_k_means_clustering,
31 connected_component_labeling,
32 touching_objects_labeling,
33 watershed_otsu_labeling,
34 binary_fill_holes,
35 invert_intensity,
36 bilateral_filter,
37 laplacian_filter,
38 laplacian_of_gaussian_filter,
39 binominal_blur_filter,
40 canny_edge_detection,
41 gradient_magnitude,
42 h_maxima,
43 h_minima,
44 otsu_multiple_thresholds,
45 regional_maxima,
46 regional_minima,
47 richardson_lucy_deconvolution,
48 wiener_deconvolution,
49 tikhonov_deconvolution,
50 rescale_intensity,
51 sobel,
52 black_top_hat,
53 white_top_hat,
54 adaptive_histogram_equalization,
55 curvature_flow_denoise,
56 relabel_component,
57 label_contour,
58 label_statistics,
59 pixel_count_map,
60 elongation_map,
61 feret_diameter_map,
62 roundness_map

File /app/env/lib/python3.9/site-packages/napari_simpleitk_image_processing/_simpleitk_image_processing.py:7, in 5 from toolz import curry 6 import inspect ----> 7 from napari_tools_menu import register_function 8 from napari_time_slicer import time_slicer 9 from napari_skimage_regionprops._all_frames import analyze_all_frames

File /app/env/lib/python3.9/site-packages/napari_tools_menu/init.py:9, in 7 import warnings 8 import napari ----> 9 import napari._qt 10 import numpy as np 11 from qtpy.QtWidgets import QMenu

File /app/env/lib/python3.9/site-packages/napari/qt/init.py:53, in 45 warn_message = trans.( 46 "\n\nnapari was tested with QT library >=5.12.3.\nThe version installed is {version}. Please report any issues with\nthis specific QT version at https://github.com/Napari/napari/issues. style="color:rgb(175,0,0)">", 47 deferred=True, 48 version=QtCore.version, 49 ) 50 warn(message=warn_message) ---> 53 from .qt_event_loop import get_app, gui_qt, quit_app, run 54 from .qt_main_window import Window

File /app/env/lib/python3.9/site-packages/napari/_qt/qt_event_loop.py:15, in 12 from qtpy.QtWidgets import QApplication 14 from .. import version ---> 15 from ..settings import get_settings 16 from ..utils import config, perf 17 from ..utils.notifications import ( 18 notification_manager, 19 show_console_notification, 20 )

File /app/env/lib/python3.9/site-packages/napari/settings/init.py:6, in 4 from ..utils.translations import trans 5 from ._base import _NOT_SET ----> 6 from ._napari_settings import NapariSettings 8 all = ['NapariSettings', 'get_settings'] 11 class _SettingsProxy:

File /app/env/lib/python3.9/site-packages/napari/settings/_napari_settings.py:9, in 7 from ..utils._base import _DEFAULT_CONFIG_PATH 8 from ..utils.translations import trans ----> 9 from ._appearance import AppearanceSettings 10 from ._application import ApplicationSettings 11 from ._base import EventedConfigFileSettings, _remove_empty_dicts

File /app/env/lib/python3.9/site-packages/napari/settings/_appearance.py:4, in 1 from pydantic import Field 3 from ..utils.events.evented_model import EventedModel ----> 4 from ..utils.theme import available_themes 5 from ..utils.translations import trans 6 from ._fields import Theme

File /app/env/lib/python3.9/site-packages/napari/utils/init.py:2, in 1 from ._dask_utils import resize_dask_cache ----> 2 from .colormaps import Colormap 3 from .info import citation_text, sys_info 4 from .notebook_display import nbscreenshot

File /app/env/lib/python3.9/site-packages/napari/utils/colormaps/init.py:2, in 1 from .colorbars import make_colorbar ----> 2 from .colormap import Colormap 3 from .colormap_utils import ( 4 ALL_COLORMAPS, 5 AVAILABLE_COLORMAPS, (...) 16 matplotlib_colormaps, 17 )

File /app/env/lib/python3.9/site-packages/napari/utils/colormaps/colormap.py:11, in 9 from ..translations import trans 10 from .colorbars import make_colorbar ---> 11 from .standardize_color import transform_color 14 class ColormapInterpolationMode(str, Enum): 15 """INTERPOLATION: Interpolation mode for colormaps. 16 17 Selects an interpolation mode for the colormap. (...) 21 bin between by neighboring controls points. 22 """

File /app/env/lib/python3.9/site-packages/napari/utils/colormaps/standardize_color.py:27, in 24 from typing import Any, Callable, Dict, Sequence 26 import numpy as np ---> 27 from vispy.color import ColorArray, get_color_dict, get_color_names 28 from vispy.color.color_array import _string_to_rgb 30 from ..translations import trans

File /app/env/lib/python3.9/site-packages/vispy/color/init.py:12, in 10 from ._color_dict import get_color_names, get_color_dict # noqa 11 from .color_array import Color, ColorArray ---> 12 from .colormap import (Colormap, BaseColormap, # noqa 13 get_colormap, get_colormaps) # noqa 15 all = ['Color', 'ColorArray', 'Colormap', 'BaseColormap', 16 'get_colormap', 'get_colormaps', 17 'get_color_names', 'get_color_dict']

File /app/env/lib/python3.9/site-packages/vispy/color/colormap.py:14, in 12 from hsluv import hsluv_to_rgb 13 from ..util.check_environment import has_matplotlib ---> 14 import vispy.gloo 16 ############################################################################### 17 # Color maps 18 19 # Length of the texture map used for luminance to RGBA conversion 20 LUT_len = 1024

File /app/env/lib/python3.9/site-packages/vispy/gloo/init.py:47, in 5 """ 6 Object oriented interface to OpenGL. 7 (...) 42 43 """ 45 from future import division ---> 47 from . import gl # noqa 48 from .wrappers import * # noqa 49 from .context import (GLContext, get_default_config, # noqa 50 get_current_canvas) # noqa

File /app/env/lib/python3.9/site-packages/vispy/gloo/gl/init.py:230, in 228 from . import gl2 as default_backend # noqa 229 if default_backend._lib is None: # Probably Android or RPi --> 230 from . import es2 as default_backend # noqa 233 # Call use to start using our default backend 234 use_gl()

File /app/env/lib/python3.9/site-packages/vispy/gloo/gl/es2.py:48, in 46 # Else, we failed and exit 47 if es2_file is None: ---> 48 raise OSError('GL ES 2.0 library not found') 49 # Load it 50 _lib = ctypes.CDLL(es2_file)

OSError: GL ES 2.0 library not found

opened by thawn 1
Crash caused by leaked semaphore objects
I'm getting crashes when running label statistics for datasets around size (1000, 2048, 2048) pixels.

There's some sort of memory leak, which kills ipython completely. I'd understand more if this was a truly giant dataset, or I was doing exessively complicated label statistics, instead of just label size, but that's not the case. I'm using a small, cropped subsection of my larger dataset, and I'd always considered one to two thousand pixels a fairly reasonable size to process in memory.

In [7]: zsh: killed ipython (napari-empanada) genevieb@192-168-1-103 ~ % /Users/genevieb/mambaforge/envs/napari-empanada/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown warnings.warn('resource_tracker: There appear to be %d '
opened by GenevieveBuckley 3
Filters don't take voxel size into account when called from tools menu

See: https://forum.image.sc/t/z-scale-reshape-issue/67022/6

It should be feasible to adopt the assistants strategy to copy layer scale from input to output...

opened by haesleinhuepf 0
Spatial transforms / resampling

It would be cool to implement some simple transforms: rescaling, translation, rotation and/or rigid. This notebook is a good starting point: http://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/Python_html/21_Transforms_and_Resampling.html
enhancement good first issue

opened by haesleinhuepf 0

Releases(0.4.4)

0.4.4(Sep 24, 2022)
New features

Compatibility with morphometrics: label_statistics now show up in morphometrics region properties widget

Source code(tar.gz)
Source code(zip)
0.4.3(Aug 27, 2022)
New features

Nice display of image variables in jupyter notebooks

Source code(tar.gz)
Source code(zip)
0.4.2(Aug 10, 2022)
Bugfix

Speed up label_statistics() computation and spare memory in case shape=False and/or perimeter=False (thanks to @GenevieveBuckley for reporting this issue)

Source code(tar.gz)
Source code(zip)
0.4.1(Jul 28, 2022)
Bugfix

label_statistics() now also works if the the passed intensity image is None (thanks to @GenevieveBuckley for reporting #10 )

Source code(tar.gz)
Source code(zip)
0.4.0(Jul 10, 2022)
Miscellaneous

The menu entry Morphological gradient has been moved to Tools > Filtering / edge enhancement

Dropped support for Python 3.7, added 3.10

Updated function documentation to be more biology-specific, as the napari-assistant can now search it.

Backwards compatibility breaking changes

The unused viewer parameter has been removed from all functions

Source code(tar.gz)
Source code(zip)
0.3.1(May 28, 2022)
Bugfix

parametric images were not created

Source code(tar.gz)
Source code(zip)
0.3.0(May 8, 2022)
Backwards compatibility breaking changes

The API of label_statistics was modified:

The position of the napari_viewer argument was changed. It is recommended to use it as keyword-argument.

The function now returns Pandas DataFrames instead of a dictionary.

Source code(tar.gz)
Source code(zip)
0.2.7(Apr 27, 2022)
Bug fix:

Error with parametric images (thanks to @sebi06 for reporting)

Source code(tar.gz)
Source code(zip)
0.2.5(Apr 2, 2022)
New features

Support for analysing time lapse data (frame-by-frame) in menu Tools > Measurement > Measurements of all frames (n-SsimpleITK)

Source code(tar.gz)
Source code(zip)
0.2.4(Mar 26, 2022)
Bug fixes

Label images of type uint32 caused a crash when applying Measurements

Source code(tar.gz)
Source code(zip)
0.2.3(Mar 20, 2022)

Documentation update
Source code(tar.gz)
Source code(zip)
0.2.2(Mar 20, 2022)
New features

Use the napari-assistant as graphical user interface

Source code(tar.gz)
Source code(zip)
0.2.1(Jan 30, 2022)
Bugfix:

label_statistics caused an error when called from scripts and no viewer was provided

Source code(tar.gz)
Source code(zip)
0.2.0(Jan 1, 2022)
New features

label_statistics

simple_linear_iterative_clustering

scalar_image_k_means_clustering

invert_intensity

adaptive_histogram_equalization

curvature_flow_denoise

morphological_gradient

standard_deviation_filter

relabel_component

label_contour

threshold_intermodes

threshold_kittler_illingworth

threshold_li

threshold_moments

threshold_renyi_entropy

threshold_shanbhag

threshold_yen

threshold_isodata

threshold_triangle

threshold_huang

threshold_maximum_entropy

Source code(tar.gz)
Source code(zip)
0.1.4(Dec 12, 2021)

Added __common_alias__ and simplified API for better code generation.
Source code(tar.gz)
Source code(zip)
0.1.3(Dec 4, 2021)
Backwards compatible changes

Changed menu entries to cover finer differentiation between various filtering and segmentation techniquese

Source code(tar.gz)
Source code(zip)
0.1.2(Nov 28, 2021)
New features

Input images can be dask images now. They will be converted to numpy before passing over to SimpleITK

new filters

bilateral_filter,

laplacian_filter

laplacian_of_gaussian_filter

binominal_blur_filter

canny_edge_detection

gradient_magnitude

h_maxima

h_minima

otsu_multiple_thresholds

regional_maxima

regional_minima

richardson_lucy_deconvolution

wiener_deconvolution

tikhonov_deconvolution

rescale_intensity

sobel

black_top_hat

white_top_hat

Source code(tar.gz)
Source code(zip)
0.1.1(Nov 28, 2021)
Bug fixes

Links to images were broken in the documentation

Source code(tar.gz)
Source code(zip)
0.1.0(Nov 28, 2021)
New features

median_filter

gaussian_blur

threshold_otsu

signed_maurer_distance_map

morphological_watershed

connected_component_labeling

touching_objects_labeling

watershed_otsu_labeling

binary_fill_holes

Source code(tar.gz)
Source code(zip)

Owner

Robert Haase

Computational Microscopist, BioImage Analyst, Code Jockey

GitHub

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