Commit fda9ae85 authored by Christian Tischer's avatar Christian Tischer

EMBL course

parent 899bf55d
......@@ -32,6 +32,7 @@ True or false?
* Pixel coordinates depend on image calibration.
* Pixel indices are always positive integer values.
* The lowest pixel index of a 2D image always is `[1,1]`.
* When looking at a 2D image, the lowest pixel index is in the lower left corner.
 
......@@ -39,6 +40,7 @@ True or false?
 
\pagebreak
## Image display
......@@ -100,20 +102,6 @@ Fill in the blanks, using those words: decrease, larger_than, increase, smaller_
}
'/>
### Motivation
It sometimes is necessary to change the numeric content of images. It is important to understand how to do this properly in order to avoid uncontrolled artifacts.
What are good reasons to change the pixel values in an image?
1. For intensity measurements, the image background (e.g. camera based offset) should be subtracted from all pixels.
2. For threshold based image segmentation (object detection), it helps to first filter noise in the image.
3. For intensity measurements, it helps to filter noise in the image.
4. The image appears to dark, multiplication of all pixels by a constant number is a means to make it brighter.
5. For uneven illumination (e.g. occuring in wide-field microscopy with large camera chips), one should do flat-field correction, which makes the intensity values even across the image.
6. Our microscope was broken. We took images on a replacement microscope. The pixel values were consistently higher than on our usual microscope. We multiplied the pixels on all images from the replacement microscope by a constant factor to make them comparable to our usual data.
### Activity: Pixel based background subtraction
* Open image: xy_8bit__nuclei_noisy_different_intensity.tif
......@@ -170,13 +158,6 @@ True or false?
}
'/>
### Motivation
What are good reasons to change the pixel data type of an image?
* TODO
* TODO
### Activity: 16-bit to 8-bit conversion
* Open image: xy_16bit__two_values.tif
......@@ -212,6 +193,7 @@ In order to find objects in a image, the first step often is to determine whethe
shift [fontcolor=white,color=white];
"intensity image" -> threshold;
threshold -> "binary image";
"binary image" -> "mask" [label=" aka"];
"binary image" -> "background value";
"binary image" -> "foreground value";
"background value" -> "0";
......
......@@ -112,18 +112,12 @@ tophat( image ) = image - opening( image, r ) = image - dilation( erosion( imag
- Use a tophat filter to remove local background.
- Threshold the spots in the tophat filtered image.
## Activity: Explore tophat fiter on noisy data
## 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
### Formative assessment
TODO
## Median filter for local background subtraction
<img src='https://g.gravizo.com/svg?
......@@ -140,8 +134,6 @@ TODO
median_based_background_correction = image - median( image, r)
```
### Activity: Implement median based background subtraction
- Write code to implement a median based background subtraction
......@@ -152,16 +144,13 @@ median_based_background_correction = image - median( image, r)
- Open images:
- xy_8bit__spots_local_background.tif
- xy_8bit__spots_local_background_with_noise.tif
-
- Use topHat filter to remove local background
- Use tophat filter to remove local background
- Devise code to implement a tophat filter using basic functions
### Formative assessment
TODO
## Learn more
- https://imagej.net/MorphoLibJ#Grayscale_morphological_filters
......
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