Workflow constructed from Tutorial 'Introduction to Image Analysis using Galaxy' 6.0
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This workflows is part of the tutorial Introduction to Image Analysis using Galaxy, available in the GTN
Features
- Includes Galaxy Workflow Tests
Thanks to...
Workflow Author(s): Till Korten, Leonid Kostrykin
Tutorial Author(s): Thomas Wollmann, Saskia Hiltemann, Leonid Kostrykin
Tutorial Contributor(s): Helena Rasche, Saskia Hiltemann, Björn Grüning, Leonid Kostrykin, Thomas Wollmann
Funder(s): ELIXIR Europe
Inputs
| ID | Name | Description | Type |
|---|---|---|---|
| input_image | #main/input_image | The image to be segmented |
|
Steps
| ID | Name | Description |
|---|---|---|
| 1 | Show image info | toolshed.g2.bx.psu.edu/repos/imgteam/image_info/ip_imageinfo/5.7.1+galaxy1 |
| 2 | Filter 2-D image | toolshed.g2.bx.psu.edu/repos/imgteam/2d_simple_filter/ip_filter_standard/1.12.0+galaxy1 |
| 3 | Perform histogram equalization | toolshed.g2.bx.psu.edu/repos/imgteam/2d_histogram_equalization/ip_histogram_equalization/0.18.1+galaxy0 |
| 4 | Threshold image | toolshed.g2.bx.psu.edu/repos/imgteam/2d_auto_threshold/ip_threshold/0.18.1+galaxy3 |
| 5 | Convert binary image to label map | toolshed.g2.bx.psu.edu/repos/imgteam/binary2labelimage/ip_binary_to_labelimage/0.5+galaxy0 |
| 6 | Overlay images | toolshed.g2.bx.psu.edu/repos/imgteam/overlay_images/ip_overlay_images/0.0.4+galaxy4 |
| 7 | Count objects in label map | toolshed.g2.bx.psu.edu/repos/imgteam/count_objects/ip_count_objects/0.0.5-2 |
Outputs
| ID | Name | Description | Type |
|---|---|---|---|
| out_png | #main/out_png | n/a |
|
| output | #main/output | n/a |
|
Version History
master
Creators and Submitter
Views: 1167 Downloads: 154 Runs: 0
Created: 2nd Jun 2025 at 10:57
AttributionsNone
Related items
Teams: Galaxy Training Network
Organizations: Galaxy
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Galaxy is an open, web-based platform for accessible, reproducible, and transparent computational biological research.
- Accessible: Users can easily run tools without writing code or using the CLI; all via a user-friendly web interface.
- Reproducible: Galaxy captures all the metadata from an analysis, making it completely reproducible.
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The Galaxy Training Network (GTN) is a collection of hands-on tutorials that are designed to be interactive and are built around Galaxy.
These tutorials can be used for learning and teaching how to use Galaxy for general data analysis, as well as a wide array of hands-on tutorials covering specific domains such as assembly, RNA-Seq analysis, deep learning, climate analysis, and more!
