Commit 0dc926ca by Aliaksandr Halavatyi Committed by Christian Tischer

### Pixel neighbourhood conversion

parent 45f75824
 - **[ Open... ]** "/image-analysis-training-resources/image_data/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 **[ Mean]** - Try different neighbourhood sizes for mean filter - Appreciate that the filtered pixel values are slightly wrong due to integer data type - Binarize the filtered image by applying a threshold ()
 --- title: Neighbourhood image filters layout: page permalink: /filtersneighbourhood --- # Neighborhood filters ## Requirements - Pixel properties ## Motivation This module explains how image features (objects) can be enhanced using filters layout: module prerequisites: - "[Image pixels](image_pixels)" objectives: - Understand the basic principle of a neighbourhood filter motivation: > This module explains how image features (objects) can be enhanced using filters ## Learning objectives - Understand the basic principle of a neighbourhood filter. ## Concept map ```mermaid graph TB P(pixel) --> |has| NBH(neighbourhood pixels) NBH --> |are used in| A(mathematical formula) A --> |compute new| NP(pixel value) ``` | | | | | | | | | |---|---|---|---|---|---|---|---| ... ... @@ -37,15 +19,38 @@ graph TB | | | | | NB | NB | NB | | | | | | | | | | | ## Example concept_map: > graph TB P(pixel) --> |has| NBH(neighbourhood pixels) NBH --> |are used in| A(mathematical formula) A --> |compute new| NP(pixel value) # figure: /figures/binarization.png # figure_legend: Image before and after binarization by applying a threshold. activity_preface: > Use mean filter to facilitate image binarization activities: "ImageJ GUI": "filter_nighbourhood/activities/mean_filter_imagejgui.md" # "ImageJ Macro": # "Jython": # "MATLAB": exercises_preface: > TODO: Mean filter image exercises: # "ImageJ GUI": # "ImageJ Macro": # "Jython": # "MATLAB": ## Activity: Use mean filter to facilitate image binarization learn_next: - "[Convolution filters](filter_convolution)" - "[Rank filters](filter_rank)" * 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 external_links: --- \ No newline at end of file
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