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1 change: 1 addition & 0 deletions episodes/01-opening-and-checking-an-image.md
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Expand Up @@ -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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1 change: 1 addition & 0 deletions episodes/02-applying-filters.md
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Expand Up @@ -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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1 change: 1 addition & 0 deletions episodes/03-thresholding-and-segmentation.md
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Expand Up @@ -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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1 change: 1 addition & 0 deletions episodes/04-measurements.md
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Expand Up @@ -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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1 change: 1 addition & 0 deletions episodes/05-napari.md
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Expand Up @@ -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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