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Merge pull request #69 from zivy/updateChannelArithmeticDocs
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Updating the documentation for the channel arithmetic extension.
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@zivy
zivy authored Feb 15, 2024
2 parents 5972437 + cae8aa5 commit e04a91f
Showing 1 changed file with 93 additions and 55 deletions.
148 changes: 93 additions & 55 deletions XTChannelArithmetic.py
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Original file line number Diff line number Diff line change
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def XTChannelArithmetic(imaris_id=None):

app = QApplication([])
app.setStyle(ieb.style) # Consistent setting of style for all applications
app.setStyleSheet(qdarkstyle.load_stylesheet(qt_api="pyside6"))
Expand All @@ -79,7 +78,9 @@ class ChannelArithmeticDialog(ieb.ImarisExtensionBase):
This program enables one to specify arithmetic expressions which are used to
create new channels. The basic arithmetic operations are supported: +,-,*,/,**.
More advanced operations that run short `SimpleITK <https://simpleitk.org/>`_
code snippets are also supported.
code snippets are also supported. SimpleITK provides `hundreds of filters
<https://simpleitk.readthedocs.io/en/master/filters.html>`_ that
can be used via this program.
Channels are referenced using square brackets and the channel index, starting
at **zero**. To apply an expression to all channels, use the channel index 'i'.
Expand Down Expand Up @@ -107,97 +108,134 @@ class ChannelArithmeticDialog(ieb.ImarisExtensionBase):
Basic Examples
--------------
#. Multiply channels zero and three:
Multiply channels zero and three:
.. code-block:: Python
.. code-block:: Python
[0]*[3]
[0]*[3]
#. Multiply channels zero and three and subtract the result from channel four:
Multiply channels zero and three and subtract the result from channel four:
.. code-block:: Python
.. code-block:: Python
[4] - ([0]*[3])
[4] - ([0]*[3])
#. Duplicate all channels:
Duplicate all channels:
.. code-block:: Python
.. code-block:: Python
[i]
[i]
#. Subtract channel zero from all channels:
Subtract channel zero from all channels:
.. code-block:: Python
.. code-block:: Python
[i]-[0]
[i]-[0]
#. Invert channel intensity (new intensity values are the result of subtracting the current intensity
value from the maximal possible value):
.. code-block:: Python
Advanced Examples
-----------------
sitk.InvertIntensity([1])
#. Threshold channel one using a value of 100, resulting image is binary with
values in {0,1}:
.. code-block:: Python
[1]>100
#. Threshold a specific channel to create a binary result using the Otsu filter,
resulting image is binary with values in {0,1}:
.. code-block:: Python
sitk.OtsuThreshold([1], 0, 1)
Threshold channel one using a value of 100, resulting image is binary
values in {0,1}:
#. Gaussian blurring (variance specified in metric units, e.g. nm):
.. code-block:: Python
.. code-block:: Python
[1]>100
# blur channel 3 using a Gaussian with variance of 0.245 for x, y and z
sitk.DiscreteGaussian([3], 0.245)
# blur channel 3 using a Gaussian with different variance per x=0.245, y=0.1, z=0.3
sitk.DiscreteGaussian([3], [0.245, 0.1, 0.3])
Threshold a specific channel to create a binary result using the Otsu
filter:
#. Median filtering using a radius of x=1, y=2 and z=3 (radius in which to compute the
median per dimension, pixel units):
.. code-block:: Python
sitk.Median([3], [1, 2, 3])
#. Linearly map the intensity values in the interval [windowMinimum, windowMaximum] to the interval
[outputMinimum, outputMaximum]. Values lower than windowMinimum are mapped to outputMinimum. Values
higher than windowMaximum are mapped to outputMaximum.
.. code-block:: Python
sitk.IntensityWindowing([1], windowMinimum=20, windowMaximum=200, outputMinimum=0, outputMaximum=255)
Advanced Examples
-----------------
#. Threshold a specific channel retaining the values above the threshold and
setting all values equal or lower to zero:
.. code-block:: Python
.. code-block:: Python
sitk.OtsuThreshold([1], 0, 1)
sitk.Cast([1]>100, sitk.sitkFloat32)*[1]
Threshold a specific channel retaining the values above the threshold:
#. Threshold a specific channel retaining the values above the threshold and
setting all values equal or lower to the threshold to an arbitrary value
(20 in this example):
.. code-block:: Python
.. code-block:: Python
sitk.Cast([1]>100, sitk.sitkFloat32)*[1]
sitk.Cast([1]>100, sitk.sitkFloat32)*[1] + sitk.Cast([1]<=100, sitk.sitkFloat32)*20
Threshold a specific channel, get all connected components, then
sort the components according to size, discarding those smaller than a minimum
size and create a binary mask corresponding to the largest component, which is
the first label(second largest component label is 2 etc.)
#. Threshold a specific channel, get all connected components, then
sort the components according to size, discarding those smaller than a minimum
size and create a binary mask corresponding to the largest component, which is
the first label(second largest component label is 2 etc.):
.. code-block:: Python
.. code-block:: Python
sitk.RelabelComponent(sitk.ConnectedComponent([1]>100), minimumObjectSize = 50)==1
sitk.RelabelComponent(sitk.ConnectedComponent([1]>100), minimumObjectSize = 50)==1
Create a binary mask representing the colocalization of two channels,
intensity values below 20 are considred noise:
#. Create a binary mask representing the colocalization of two channels,
intensity values below 20 are considred noise:
.. code-block:: Python
.. code-block:: Python
([1]>20)*([2]>20)
([1]>20)*([2]>20)
Create a binary mask representing the colocalization of two channels.
We are interested in all pixels in channel 2 that have a value above 20
and that are less than 1.0um away from pixels in channel 1 that have a value
above 100 (**note**: this operation yields different results when run using
a slice-by-slice approach vs. a volumetric approach):
#. Create a binary mask representing the colocalization of two channels.
We are interested in all pixels in channel 2 that have a value above 20
and that are less than 1.0um away from pixels in channel 1 that have a value
above 100 (**note**: this operation yields different results when run using
a slice-by-slice approach vs. a volumetric approach):
.. code-block:: Python
.. code-block:: Python
(sitk.Cast([2]>20, sitk.sitkFloat32) *
sitk.Abs(sitk.SignedMaurerDistanceMap([1]>100, insideIsPositive=False, squaredDistance=False, useImageSpacing=True)))<=1.0
(sitk.Cast([2]>20, sitk.sitkFloat32) *
sitk.Abs(sitk.SignedMaurerDistanceMap([1]>100, insideIsPositive=False, squaredDistance=False, useImageSpacing=True)))<=1.0
Create a binary mask using thresholding and then perform morphological
closing (dilation followed by erosion) with distance maps, useful
for filling holes:
#. Create a binary mask using thresholding and then perform morphological
closing (dilation followed by erosion) with distance maps, useful
for filling holes:
.. code-block:: Python
.. code-block:: Python
sitk.SignedMaurerDistanceMap(sitk.SignedMaurerDistanceMap([1]>100, insideIsPositive=False, squaredDistance=False, useImageSpacing=True) < 1.0, insideIsPositive=False, squaredDistance=False, useImageSpacing=True)<-1.0
sitk.SignedMaurerDistanceMap(sitk.SignedMaurerDistanceMap([1]>100, insideIsPositive=False, squaredDistance=False, useImageSpacing=True) < 1.0, insideIsPositive=False, squaredDistance=False, useImageSpacing=True)<-1.0
Create a binary mask using thresholding and then perform morphological
opening (erosion followed by dilation) with distance maps, useful
for removing small islands:
#. Create a binary mask using thresholding and then perform morphological
opening (erosion followed by dilation) with distance maps, useful
for removing small islands:
.. code-block:: Python
.. code-block:: Python
sitk.SignedMaurerDistanceMap(sitk.SignedMaurerDistanceMap([1]>100, insideIsPositive=False, squaredDistance=False, useImageSpacing=True) < -0.2, insideIsPositive=False, squaredDistance=False, useImageSpacing=True)<0.2
sitk.SignedMaurerDistanceMap(sitk.SignedMaurerDistanceMap([1]>100, insideIsPositive=False, squaredDistance=False, useImageSpacing=True) < -0.2, insideIsPositive=False, squaredDistance=False, useImageSpacing=True)<0.2
""" # noqa

def __init__(self):
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