Minor changes to filter_mean_skimage_napari.py

_includes/auto_threshold/auto_threshold_act1_skimage_napari.py

@@ -1,5 +1,5 @@
-# %% [markdown]
-# ## Apply manual and automated thresholds
+# %% 
+# Apply manual and automated thresholds
 
 # %%
 # initial imports
@@ -8,31 +8,23 @@
 from OpenIJTIFF import open_ij_tiff
 
 # %%
-# Read the images
-image1, axes1, scales1, units1 = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_without_offset.tif')
-image2, axes2, scales2, units2 = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_with_offset.tif')
+# Read two images
+image1, *_ = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_without_offset.tif')
+image2, *_ = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_with_offset.tif')
 
 # %%
-# Inspect image data type and values
-print(image1.dtype, image1.shape, np.min(image1), np.max(image1))
-print(image2.dtype, image2.shape, np.min(image2), np.max(image2))
+# Inspect image data type and value range
+print(image1.dtype, image1.shape, image1.min(), image1.min())
+print(image2.dtype, image2.shape, image2.min(), image2.max())
 
 # %%
 # Instantiate the napari viewer and display the images
 viewer = napari.Viewer()
 
-
 # %%
 # Add the images
-viewer.add_image(image1, name='image1')
-viewer.add_image(image2, name='image2')
-
-# %%
-# Explore the values of the image
-info_type = np.iinfo(image1.dtype)
-print('\n', info_type)
-print('\n Min image1 %d max image1 %d' % (image1.min(), image1.max()))
-print('\n Min image2 %d max image2 %d' % (image2.min(), image2.max()))
+viewer.add_image(image1, name="image1")
+viewer.add_image(image2, name="image2")
 
 # %%
 # Show the histogram
@@ -73,6 +65,6 @@
 viewer.add_image(mean_thresholded1, name='mean_thresholded1', opacity = 0.4, colormap='magenta')
 viewer.add_image(mean_thresholded2, name='mean_thresholded2', opacity = 0.4, colormap='magenta')
 
-# %% [markdown]
-# Explore other thresholding options \
+# %% 
+# Explore other thresholding options
 # Note that there exists a threshold_multiotsu function to handle cases with multi-peaks histograms

_includes/filter_neighbourhood/filter_mean_skimage_napari.py

@@ -1,5 +1,5 @@
-# %% [markdown]
-# ## Applying mean filters to an image
+# %% 
+# Apply mean filters to an image to aid foreground background segmentation 
 
 # %%
 # Instantiate the napari viewer
@@ -9,46 +9,48 @@
 
 # %%
 # Read the intensity image
-image, axes, scales, units = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_very_noisy.tif')
+image, *_ = open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_very_noisy.tif')
 
 # %%
-# View the intensity image
+# View the image 
+# - Appreciate that is is quite noisy
+# - Inspect the pixel values to find a threshold that separates the nuclei from the background
 viewer.add_image(image)
 
 # %%
-# Appreciate that one cannot segment the nuclei by a simple intensity threshold
+# Binarise the image
+# - Appreciate that one cannot segment the nuclei by a simple intensity threshold
 binary_image = image > 40
 viewer.add_image(binary_image)
 
 # %% 
-# Define a circular structural element with a radius of 1 pixel
+# Prepare filtering the image by defining a circular structural element with a radius of 1 pixel
 from skimage.morphology import disk
-radius = 1
-disk_radius1 = disk(radius)
-print(disk_radius1)
+disk_radius_1 = disk(1)
+print(disk_radius_1)
 
 # %%
-# Apply a mean filter with the structural element
+# Apply a mean filter to the image with the above structural element
 from skimage.filters.rank import mean
-mean_disk_radius1 = mean(image, disk_radius1)
+mean_image_1 = mean(image, disk_radius_1)
 
 # Add the filtered image to napari
-# Napari: Zoom in to appreciate that the filtered image 
-# contains the local mean values of the raw image
-viewer.add_image(mean_disk_radius1)
+# Napari:
+# - Zoom in to appreciate that the filtered image contains the local mean values of the raw image
+viewer.add_image(mean_image_1)
 
 # %%
-# One still cannot readily segment the nuclei
-# (rerun the below code with different thresholds)
-binary_mean_disk_radius1 = mean_disk_radius1 > 35
-viewer.add_image(binary_mean_disk_radius1)
+# Binarise the filtered image
+# - Appreciate that one still cannot readily segment the nuclei
+binary_image_1 = mean_image_1 > 35
+viewer.add_image(binary_image_1)
 
 # %% 
 # Apply mean filter with a disk of radius 3
-mean_disk_radius3 = mean(image, disk(3))
-viewer.add_image(mean_disk_radius3)
+mean_image_3 = mean(image, disk(3))
+viewer.add_image(mean_image_3)
 
 # %%
 # Now the nuclei can be segmented
-binary_mean_disk_radius3 = mean_disk_radius3 > 32
-viewer.add_image(binary_mean_disk_radius3)
+binary_image_3 = mean_image_3 > 32
+viewer.add_image(binary_image_3)