Opening and Checking an Image
- Common image formats can usually all be loaded in the same way with skimage
- Specialised proprietary formats may require specialised libraries
- Basic metrics of an image include histogram, shape and max/min pixel values
- These metrics can help tell us how the miage should be analysed
- Lookup tables can change how a single-channel image is rendered
- An RGB image contains 3 channels for red, green and blue
Applying Filters
- There are many ways of smoothing an image
- Different methods will perform better in different situations
Thresholding and Segmentation
- Thresholding, labelling and feature isolation are all forms of segmentation
- Different thresholding algorithms can perform differently in different situations
- Quantitative image analysis often requires us to individually label features
- Processing such as watershed transforms can solve cases where features of interest are very close or stuck together
Measurements
- Binary multiplication and
numpy.whereare powerful ways of combining images and masks - The numbers assigned during labelling can be used to select and process one feature at a time
Introduction to Napari
- Napari works with layers, each of which represents an image channel
- Napari doesn’t always know how to load a multi-channel image from the GUI
- We can use the Python console to perform custom operations that can’t be done in the GUI
- Most operations we performed earlier in Python can also be done in Napari, either graphically or in the terminal