Commit b4009968 authored by Christian Tischer's avatar Christian Tischer

Merge branch 'pixel_neighbourhood_conversion' into 'master'

Pixel neighbourhood conversion

See merge request !40
parents 45f75824 0dc926ca
Pipeline #10611 passed with stage
in 1 minute and 12 seconds
- **[ 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:
---
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