tutorial.md

layout	title	zenodo_link	level	questions	objectives	key_points	time_estimation	follow_up_training	contributors
tutorial_hands_on	Introduction to image analysis using Galaxy	https://zenodo.org/record/3362976	Introductory	How do I use Galaxy with imaging data? How do I convert images using Galaxy? How do I display images in Galaxy? How do I filter images in Galaxy? How do I segment simple images in Galaxy?	How to handle images in Galaxy. How to perform basic image processing in Galaxy.	The **Image Info** tool can provide valuable metadata information of an image. TIFF files can not directly viewed in the browser, but have to be converted. For visualization, images with a bit-depth more than 8-bit have to be histogram equalized.	1H	type topic_name tutorials internal imaging hela-screen-analysis	thomaswollmann shiltemann

Introduction

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Image analysis is the extraction of meaningful information from images by means of digital image processing techniques. Imaging is an important component in a wide range of scientific fields of study, such as astronomy, medicine, physics, biology, geography, chemistry, robotics, and industrial manufacturing.

This tutorial shows how to use Galaxy to perform basic image analysis tasks such as format conversion, image enhancement, segmentation, and feature extraction.

Agenda

In this tutorial, we will deal with:

1. TOC {:toc}

{: .agenda}

Getting data

The dataset required for this tutorial is available from Zenodo and contains a screen of DAPI stained HeLa nuclei (more information). We will use a sample image from this dataset for training basic image processing skills in Galaxy.

Our objective is to automatically count the number of cells contained in this image. In order to achieve this, we will enhance the quality of the image, automatically detect the nuclei and segment the nuclei and count them.

{% icon hands_on %} Hands-on: Data upload

1. If you are logged in, create a new history for this tutorial

   {% include snippets/create_new_history.md %}

2. Import the following dataset from Zenodo or from the data library (ask your instructor).

   • Important: Choose the type of data as zip.

   https://zenodo.org/record/3362976/files/B2.zip
   

   {% include snippets/import_via_link.md %} {% include snippets/import_from_data_library.md %}

3. Unzip file {% icon tool %} with the following parameters:

   • {% icon param-file %} "input_file": Zipped input file
   • "Extract single file": Single file
   • "Filepath": B2--W00026--P00001--Z00000--T00000--dapi.tif

4. Rename {% icon galaxy-pencil %} the dataset to input.tif

   {% include snippets/rename_dataset.md %} {: .hands_on}

Image Metadata Extraction

Now, we can extract metadata from an image.

{% icon hands_on %} Hands-on: Extract Image Metadata

1. Image Info {% icon tool %} with the following parameters to extract metadata from the image:

   • {% icon param-file %} "Input Image": input.tif file (output of the previous step)

2. Click on the {% icon galaxy-eye %} (eye) icon next to the file name, to look at the file content and search for image acquisition information

   {% icon question %} Questions

   1. What is the datatype?
   2. What are the pixel dimentions?
   3. How many bits per pixel are used?

   {% icon solution %} Solution

   1. TIFF
   2. 1344x1024
   3. 16 {: .solution } {: .question} {: .hands_on}

Image Conversion

Not all tools can handle all image formats. Especially proprietary microscope image formats should be converted to TIFF (supported formats). However, TIFF can not be displayed in the browser. Therefore, we convert input.tif to a PNG for visualization.

{% icon hands_on %} Hands-on: Convert Image

1. Convert image {% icon tool %} with the following parameters to convert the image to PNG:
   • {% icon param-file %} "Input Image": input.tif file
   • "Output data type": PNG
2. Rename {% icon galaxy-pencil %} the generated file to viz_input
3. Click on the {% icon galaxy-eye %} (eye) icon next to the file name to look at the file content

{: .hands_on}

Your image should look something like this:

{: width="75%"}

{% icon question %} Questions

You can observe that the image content is barely visible. Why?

{% icon solution %} Solution

The original image is 16-bit and the intensity values are spread over a larger range than the display can render. Therefore, for improved visibility the intensity histogram of the image can be normalized first. {: .solution } {: .question}

Next we will normalize the histogram to improve the contrast. We do this using a Contrast Limited Adaptive Histogram Equalization (CLAHE) approach.

{% icon hands_on %} Hands-on: Normalize Histogram and Convert Image

1. Histogram equalization {% icon tool %} with the following parameters to normalize the histogram of the image:
   • {% icon param-file %} "Source file": input.tif file
   • "Histogram Equalization Algorithm": CLAHE
2. Rename {% icon galaxy-pencil %} the generated file to input_normalized
3. Convert image {% icon tool %} with the following parameters to convert the image to PNG:
   • {% icon param-file %} "Input Image": input_normalized file (output of Histogram equalization {% icon tool %})
   • "Output data type": PNG
4. Rename {% icon galaxy-pencil %} the generated file to viz_normalized
5. Click on the {% icon galaxy-eye %} (eye) icon next to the file name, to look at the file content {: .hands_on}

Your image should now look something like this:

{: width="75%"}

We can now clearly make out the presence of the stained nuclei. Next we will automatically detect these features and segment the image.

Image Filtering

Specific features of interest (e.g., edges, noise) can be enhanced or suppressed by using an image filter.

{% icon hands_on %} Hands-on: Filter image

1. Filter Image {% icon tool %} with the following parameters to smooth the image:
   • "Image type": Gaussian Blur
   • "Radius/Sigma": 3
   • {% icon param-file %} "Source file": input.tif file
2. Rename {% icon galaxy-pencil %} the generated file to input_smoothed
3. Histogram equalization {% icon tool %} with the following parameters to normalize the histogram of the image:
   • {% icon param-file %} "Source file": input_smoothed file (output of Filter image {% icon tool %})
   • "Histogram Equalization Algorithm": CLAHE
4. Rename {% icon galaxy-pencil %} the generated file to input_smoothed_normalized
5. Convert image {% icon tool %} with the following parameters to convert the image to PNG:
   • {% icon param-file %} "Input Image": input_smoothed_normalized file (output of Histogram equalization {% icon tool %})
   • "Output data type": PNG
6. Rename {% icon galaxy-pencil %} the generated file to viz_smoothed_normalized
7. Click on the {% icon galaxy-eye %} (eye) icon next to the file name, to look at the file content and compare the result with viz_normalized. You can observe that viz_smoothed_normalized has significant reduced noise. {: .hands_on}

Your image should now look something like this:

{: width="75%"}

Segmentation

Objects of interest like nuclei can be segmented by using a smoothed image and thresholding. Moreover, the results can be overlayed with the original image.

{% icon hands_on %} Hands-on: Segment image

1. Auto Threshold {% icon tool %} with the following parameters to segment the image:

   • {% icon param-file %} "Source file": input_smoothed file (output of Filter image {% icon tool %})
   • "Threshold Algorithm": Otsu
   • "Dark Background": Yes

2. Rename {% icon galaxy-pencil %} the generated file to input_segmented

3. Binary 2 Label {% icon tool %} with the following parameters to segment the image:

   • {% icon param-file %} "Binary Image File": input_segmented file (output of Auto Threshold {% icon tool %})

4. Rename {% icon galaxy-pencil %} the generated file to input_segmented_labeled

5. Convert image {% icon tool %} with the following parameters to convert the image to PNG:

   • {% icon param-file %} "Input Image": input_segmented_labeled file (output of Binary 2 Label {% icon tool %})
   • "Output data type": PNG

6. Rename {% icon galaxy-pencil %} the converted image to viz segmented

   {% icon question %} Questions

   1. What does Binary 2 Label {% icon tool %} do? (Hint: check the tool help section)
   2. View the viz_segmented image from the last step, what do you see?
      • Can you explain this result?
   3. Exercise: Try to make the information in this image better visible (Hint: Histogram Equalization {% icon tool %})

   {% icon solution %} Solution

   1. The tool assigns each connected component (e.g., segmented cell) in the image an object id and stores it as the intensity value.

   2. The image looks completely black. The object IDs generated by Binary 2 Label {% icon tool %} are relatively low. Since the IDs are stored as intensity values, these are too low to be visible in this case. Nevertheless, there is more information in this image than meets the eye.

   3. To make labeled objects visible, the values have to be stretched to a larger range of visible intensity values. We can do that by equalizing the histogram again:

      • Histogram equalization {% icon tool %} with the following parameters to normalize the intensity values:

        • {% icon param-file %} "Source file": input_segmented_labeled file (output of Binary 2 Label {% icon tool %})
        • "Histogram Equalization Algorithm": CLAHE

      • Convert image {% icon tool %} with the following parameters to convert the image to PNG:

        • {% icon param-file %} "Input Image": output of Histogram Equalization {% icon tool %}
        • "Output data type": PNG

      The information contained in the original image has now become visible to the human eye:

   {: .solution } {: .question}

7. Overlay Segmentation Mask {% icon tool %} with the following parameters to convert the image to PNG:

   • {% icon param-file %} "Image Source File": viz_normalized file
   • {% icon param-file %} "Mask Source File": viz_segmented file
   • "Image Is Greyscale": Yes
   • "Thickness": 3
   • "Stroke Color": red
   • "Plot Labels": yes
   • "Label Color": yellow

8. Click on the {% icon galaxy-eye %} (eye) icon next to the file name, to look at the file content and assess the segmentation performance

9. Count Objects {% icon tool %} with the following parameters to count the segmented objects in the image:

   • {% icon param-file %} "Source file": input_segmented_labeled file (output of Binary 2 Label {% icon tool %})

   {% icon question %} Questions

   How many objects were segmented?

   {% icon solution %} Solution

   The Count Objects {% icon tool %} tool counted 425 objects. {: .solution } {: .question} {: .hands_on}

The resulting image should look something like this:

{: width="75%"}

We see the segmentation mask overlayed; each detected object (nucleus) is labeled with its ID value.

We see that with the help of just a few simple steps, we were able to detect the locations of the stained nuclei, and count them.

Conclusion

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In this exercise you imported images into Galaxy, extracted meta information from an image, converted between file formats, learned how to visualize microscopy images, filtered the image, and segmented cells using Galaxy.