Commit e86730fc by Christian Tischer

### Add improvements for Turku course

parent 5fe1d255
Pipeline #10112 passed with stage
in 1 minute and 5 seconds
 --- title: Image calibration layout: page --- ## Image calibration ### Activity: Explore image calibration * Open image: xy_8bit__nuclei_noisy_different_intensity.tif * Add image calibration * Explore whether and how this affects image display and measurements (e.g. distance between two points) ### Activity: Explore anisotropic 3D image data * Open image: xy_8bit_calibrated_anisotropic__mri_stack.tif * Appreciate that the pixels are anisotropic ### Formative assessment True or false? * Changing the image calibration changes the pixel values. * Pixel coordinates depend on image calibration.
 ... ... @@ -4,14 +4,10 @@ layout: page permalink: /filtersneighbourhood --- # Neighborhood filters ## Requirements To understand this episode you need to know: - Pixel properties ## Motivation ... ... @@ -20,18 +16,17 @@ This module explains how image features (objects) can be enhanced using filters ## Learning objectives - Understand common filter principles. - test mean filter on noisy image data - Understand the basic principle of a neighbourhood filter. ## Concept map ```mermaid graph TB pixel --> neighbours[neighbourhood pixels] neighbours --> better["`better` value for central pixel"] better --> update[updated pixel] P(pixel) --> |has| NBH(neighbourhood pixels) NBH --> |are used in| A(mathematical formula) A --> |compute new| NP(pixel value) ``` | | | | | | | | | |---|---|---|---|---|---|---|---| | NC | NC | NC | | | | | | ... ... @@ -42,6 +37,15 @@ graph TB | | | | | NB | NB | NB | | | | | | | | | | | ## Example TODO: Mean filter image ## Activity: Use mean filter to facilitate image binarization * Open image: xy_8bit__nuclei_noisy_different_intensity.tif * Appreciate that you cannot readily apply a threshold to binarize the image into two nuclei and background * Apply a mean filter, exploring different neighbourhood sizes * Appreciate that the filtered pixel values are slightly wrong due to integer data type * Binarize the filtered image by applying a threshold ## Activity TODO: construct example with mean filter on noisy data. \ No newline at end of file
 --- title: Image math title: Pixel processing layout: page --- ## Image math ## Pixel processing ```mermaid graph TD PV(pixel values) --> MO(mathematical operation) MO --- |e.g.|SV(subtract value) PV --> |have| DT(data type) MO --> DPV(result pixel values) DT --> |restricts| DPV PV(pixel values) --> PO(processing operation) PO -.- |e.g.| SV(subtract value) PO --> |replaces| PV(pixel values) ``` ```mermaid graph TD PV("pixel values") --> MO("processing operation") MO --> NPV("new image") ``` ```mermaid graph TD PO("processing operation") --> |changes| PV(pixel values) DT("data type") --> |limits| PV ``` ### Activity: Pixel based background subtraction ... ...
 ```mermaid graph TD VV(Voxel values) --> |accessed by| VI(Voxel indices) VV(Voxel values) --> |accessed by| RWC(Real world coordinates) ```
 --- title: Image spatial calibration layout: page --- ## Image calibration ```mermaid graphTD VV(Voxel indices) -->|multiplied by| VS(Voxel spacing) VS --> |yields|RWC(Calibrated voxel coordinates) '/> ### Example ### Activity: Explore image calibration * Open image: xyz_8bit_calibrated_anisotropic__mri_head.tif * Check the calibration of this image * Explore how image calibration affects spatial measurements, e.g., * Measure the distance between two pixels in the image * Measure the size of an image region * Appreciate that image calibration might be neccessary, e.g. * 3D distance measurements * Appreciate that image calibration can be confusing, e.g. * not consistently used in image filter parameter specification ### Formative assessment Answer below questions: * Given a voxel spacing of (100 nm, 100 nm, 300 nm), what is the distance between the voxels at indices [10, 5, 11] and [2, 20, 13]?
 ... ... @@ -3,9 +3,12 @@ ## Modules image_pixels.md calibration.md (clean up concept map) pixel_calibration.md image_display.md pixel_data_types.md image_math.md pixel_processing.md binarization.md connected_components.md measure_shapes.md measure_intensities.md filter_neighbourhood.md
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