Local background subtraction
Requirements
- Neighbourhood filters
- Rank filters
- Convolutional filters
- Pixel math
Motivation
This module explains how to remove background which has different values in different image parts.
Learning objectives
- Understand how to use filters to create background image
- Learn differences between different methods/filters for generating background image
- Practice background subtraction
Concept map
graph TD;
image --> smooth1[small radius filter]
image --> smooth2[big radius filter]
smooth1 --> image1[Noise suppressed image]
smooth2 --> image2[Background image]
image1 --> subtraction["[Noise suppressed image] - [Background image]"]
image2 --> subtraction
subtraction --> result[Background subtracted image]
Possible filters for creating bacground image
- Median filter
- Opening filter: the result of background subtraction operation is called Top-Hat filter
- Gaussian filter
Activity: Implement a tophat filter
- Devise code implementing a tophat filter, using minimum and maximum filters
Activity: Explore tophat filter
- Open image: xy_8bit__spots_local_background.tif
- Use a tophat filter to remove local background
Activity: Explore tophat filter on biological data
- Open image: xy_16bit__autophagosomes.tif
- Appreciate that you cannot readliy segment the spots.
- Use a tophat filter to remove local background.
- Threshold the spots in the tophat filtered image.
Activity: Explore tophat filter on noisy data
- Open image: xy_8bit__spots_local_background_with_noise.tif
- Use topHat filter to remove local background
- Appreciate that noise poses a challenge to the tophat filter
Activity: Implement median based background subtraction
- Write code to implement a median based background subtraction
Activity: Explore median filter for local background subtraction
- Open images:
- xy_8bit__spots_local_background.tif
- xy_8bit__spots_local_background_with_noise.tif
- Use tophat filter to remove local background
- Devise code to implement a tophat filter using basic functions
Formative assessment
Answer below questions. Discuss with your neighbour!
- What could one do to close small gaps in a binary image?
- What could one do to remove small objects in a image?
- What could you use for local background subtraction in a very noisy image?