Commit 43d92595 by Toby Hodges

### convert to module layout

parent 70f0909b
 --- --- title: Morphological filters title: Morphological Filters layout: page layout: module permalink: /filtermorphological prerequisites: - "[Neighbourhood filters](filter_neighbourhood)" - "[Rank filters](filter_rank)" objectives: - "Design morphological filters using rank filters" - "Execute morphological filters on binary or grayscale images and explain the output" motivation: > Filters can be used to change size and shape of objects in an image. [*] Concept map below assumes bright objects on dark background. For dark objects on bright background effect of min and max filters is inverted. concept_map: > graph TD image --> max1[max] image --> min1[min] image --> max2[max] image --> min2[min] 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 d --> gradient subgraph morphological filter name dilation erosion opening closing gradient end # figure: TODO # figure_legend: TODO activity_preface: > Open the image, `xy_8bit_binary_two_spots_different_size.tif`, and explore how structures grow and shrink, using erosion and dilation # activities: # "Platform": "path/to/file.md" exercises_preface: > ### Exercise 1 Fill in the blanks, using those words: "shrinks", "increases", "decreases", "enlarges". 1. An erosion _____ objects in a binary image. 2. An erosion in a binary image _____ the number of foreground pixels. 3. A dilation in a grayscale image _____ the average intensity in the image. 4. A dilation _____ objects in a binary image. ### Exercise 2 True of false? Discuss with your neighbour! 1. Morphological openings on binary images can decrease the number of foreground pixels. 2. Morphological closings on binary images never decreases the number of foreground pixels. 3. Performing a morphological closing a twice in a row does not make sense, because the second closing does not further change the image. # exercises: # "Platform": "path/to/file.md" learn_next: - TODO external_links: - "[Wikipedia: Morphological Gradient](https://en.wikipedia.org/wiki/Morphological_gradient)" - "[ImageJ docs: Greyscale morphological filters](https://imagej.net/MorphoLibJ#Grayscale_morphological_filters)" --- --- # Morphological filters ## Requirements - Neighbourhood filters - Rank filters ## Motivation This module explains how filters can be used to change size and shape of objects in the image. ## Learning objectives - 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 image --> max1[max] image --> min1[min] image --> max2[max] image --> min2[min] 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 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 - Explore effects of morphological closing and opening: - closing can fill holes - closing can connect gaps - opening can remove thin structures ### Formative assessment Fill in the blanks, using those words: shrinks, increases, decreases, enlarges. 1. An erosion _____ objects in a binary image. 2. An erosion in a binary image _____ the number of foreground pixels. 3. A dilation in a grayscale image _____ the average intensity in the image. 4. A dilation _____ objects in a binary image. True of false? Discuss with your neighbour! 1. Morphological openings on binary images can decrease the number of foreground pixels. 2. Morphological closings on binary images never decreases the number of foreground pixels. 3. Performing a morphological closing a twice in a row does not make sense, because the second closing does not further change the image. ## Learn more - https://en.wikipedia.org/wiki/Morphological_gradient - https://imagej.net/MorphoLibJ#Grayscale_morphological_filters
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