Commit c87a021f authored by Christian Tischer's avatar Christian Tischer

Turku

parent e86730fc
Pipeline #10125 passed with stage
in 58 seconds
......@@ -19,44 +19,42 @@ This module explains how filters can be used to change size and shape of objects
- Understand how to design morphological filters using rank filters
- Execute morpholofical filters on binary or grayscale images and explain the output
## Concept map
```mermaid
graph TD;
graph TD
image --> max1[max]
image --> min1[min]
image --> max2[max]
image --> min2[min]
subgraph one or more rank filters applied sequentially
image --> d
subgraph rank filter sequence
max2 --> min3[min]
min2 --> max3[max]
max1
min1
d[max - min]
end
max1 --> dilation
min1 --> erosion
max3 --> opening
min3 --> closing
subgraph result
d --> gradient
subgraph morphological filter name
dilation
erosion
opening
closing
gradient
end
```
[*] Concept map above assumes bright objects on dark background. For dark objects on bright background effect of min and max filters inverses
### Activity: Explore erosion and dilation on binary images
- Open image: xy_8bit_binary__two_spots_different_size.tif
- Explore how structures grow and shrink, using erosion and dilation
### Activity: Explore opening and closing on binary images
- Open image: xy_8bit_binary__for_open_and_close.tif
......@@ -65,7 +63,6 @@ graph TD;
- closing can connect gaps
- opening can remove thin structures
### Formative assessment
Fill in the blanks, using those words: shrinks, increases, decreases, enlarges.
......
......@@ -4,7 +4,6 @@ layout: page
permalink: /filterrank
---
# Rank filters
## Basic rank filters
......@@ -67,4 +66,7 @@ True or false? Discuss with your neighbour!
2. Small structures can completely disappear from an image when applying a median filter.
### Learn next
- median based local background subtraction
......@@ -9,13 +9,13 @@ permalink: /localbackground
## 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
......@@ -39,6 +39,7 @@ graph TD;
```
## Possible filters for creating bacground image
- Median filter
- Opening filter: the result of background subtraction operation is called **Top-Hat filter**
- Gaussian filter
......@@ -53,7 +54,6 @@ graph TD;
- 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
......@@ -72,7 +72,6 @@ graph TD;
- Write code to implement a median based background subtraction
### Activity: Explore median filter for local background subtraction
- Open images:
......
......@@ -5,9 +5,17 @@ layout: page
## Intensity measurements
### Concept map
```mermaid
graph TD
(TODO)
```
### Activity: Measure intensities in image regions
* Open image: xy_float__h2b_bg_corr.tif
* Appreciate that this image is already background corrected.
* Measure for both nuclei:
* Maximum intensity
* Average intensity
......@@ -17,7 +25,7 @@ layout: page
* Discuss where to measure!
### Activity: Intensity measurements without pixel based background correction
### Optional activity: Intensity measurements without pixel based background correction
#### Motivation
......
---
title: Image spatial calibration
title: Spatial image calibration
layout: page
---
......
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