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lauracmurphy · lauracmurphy · commit 471af03cfc80 · 2026-04-30T13:47:45.000+01:00
diff --git a/episodes/01-opening-and-checking-an-image.md b/episodes/01-opening-and-checking-an-image.md
@@ -469,3 +469,4 @@ out the pixel size.
 - Lookup tables can change how a single-channel image is rendered
 - An RGB image contains 3 channels for red, green and blue
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diff --git a/episodes/02-applying-filters.md b/episodes/02-applying-filters.md
@@ -242,3 +242,4 @@ subsequent chapters for thresholding and segmentation!
 - There are many ways of smoothing an image
 - Different methods will perform better in different situations
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diff --git a/episodes/03-thresholding-and-segmentation.md b/episodes/03-thresholding-and-segmentation.md
@@ -418,3 +418,4 @@ harder to segment than even, solid, rounded shapes.
 - Processing such as watershed transforms can solve cases where features of interest
   are very close or stuck together
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diff --git a/episodes/04-measurements.md b/episodes/04-measurements.md
@@ -87,3 +87,4 @@ A few notes:
 - Binary multiplication and `numpy.where` are powerful ways of combining images and masks
 - The numbers assigned during labelling can be used to select and process one feature at a time
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diff --git a/episodes/05-napari.md b/episodes/05-napari.md
@@ -272,3 +272,4 @@ you would need to add to get it to produce tangible output.
 - Most operations we performed earlier in Python can also be done in Napari,
   either graphically or in the terminal
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