How to process scientific images on linux!

The name Fiji is a cyclic abbreviation, much like GNU. Stands for "Fiji Is Just ImageJ". ImageJ is a useful tool for image analysis in scientific research—for example, you can use it to identify tree types in aerial landscapes. ImageJ can classify items. It is built with a plug-in architecture, and a large number of plug-ins are available to increase the flexibility of use.

The first step is to install ImageJ (or Fiji). This package is available with most ImageJ distributions. If you wish, you can install it this way and then install the required standalone plugins based on your research. Another option is to get the most commonly used plugins while installing Fiji. Unfortunately, most Linux distributions will not have a Fiji installation package available in their software center. Fortunately, the simple installation file on the official website is available. This is a zip file that contains all the files and directories needed to run Fiji. When you first launch it, you'll just see a toolbar with menu items listed. (figure 1)

Figure 1. When you open Fiji for the first time, there is a minimized interface.

If you don't have images ready to practice using ImageJ, the Fiji installation package includes some example images. Click on the File->Open Samples drop-down menu option (Figure 2). These examples cover many tasks you may be interested in doing.

Figure 2. Case pictures for learning to use ImageJ.

If you install Fiji instead of just ImageJ, a large number of plug-ins will also be installed. The first thing to note is the auto-updater plugin. Every time you open ImageJ, the plugin will check online for updates to ImageJ and installed plugins.

All installed plug-ins are selectable in the "Plug-ins" menu item. Once you have a lot of plugins installed, the list can get overwhelming, so streamline your selection of plugins. If you want to update manually, click the "Help"->"Update Fiji" menu item to force detection and obtain a list of available updates (Figure 3).

Figure 3. Force manual detection of available updates.

So, what can you do with Fiji/ImageJ now? For example, count the number of items in a picture. You can load samples by clicking "File"->"Open Samples"->"Embryos".

Figure 4. Use ImageJ to count the number of items in the picture.

The first step is to set the proportions of the image so that you can tell ImageJ how to identify items. First, select the Select Line button on the toolbar. Then select "Analyze"->"Set Scale", and then the number of pixels included in the scale will be set (Figure 5). You can set the "known distance" to 100 and the unit to "um".

Figure 5. Many image analysis tasks require setting a range for the image.

The next step is to simplify the information within the image. Click "Image"->"Type"->"8-bit" to reduce the amount of information to an 8-bit grayscale image. To separate independent objects click "Process"->"Binary"->"Make Binary" to automatically set the image threshold. (Figure 6).

Figure 6. Some tools can automate tasks like thresholding.

Before counting the items in the image, you need to remove manual operations like scale bars. This can be done by selecting it with the Rectangular Selection Tool and clicking "Edit" -> "Clear". Now you can analyze the image to see what object is here.

Make sure no area in the image is selected, click "Analyze"-> "Analyze Particles" to pop up a window to select the minimum size, which determines what the final image will show (Figure 7).

*Figure 7. You can generate a reduced image by determining the minimum size. *

Figure 8 shows an overview in the summary window. Each minpoint also has its own detail window.

Figure 8. Output containing an overview list of known minimum points.

When you have an analysis program that works on a given image type, you usually need to apply the same steps to a series of images. This could be thousands, and you certainly don't want to manually repeat this for every image. At this point, you can group the necessary steps into macros so they can be applied multiple times. Click Plug-in->"Macros"->"Record", a new window will pop up to record all your subsequent commands. After all steps are completed, you can save it as a macro file and run it repeatedly on other images by clicking "Plugins"->"Macros"->"Run".

If you have a very specific step to work on, you can simply open the macro file and edit it manually since it is a simple text file. In fact, there is an entire macro language that allows you to more fully control the image processing process.

However, if you have a really large series of images to process, this will also be tedious work. In this case, go to "Process"->"Batch"->"Macro" and a new window will pop up where you can set up batch processing jobs (Figure 9).

Figure 9. Run macro with a single command for batch input images.

In this window, you can choose which macro file to apply, the source directory where the input image is located, and the output directory where you want to write the output image. You can also set the output file format and filter the input images by file name. After everything is ready, click the "Process" button at the bottom of the window to start batch operations.

If this is a job that will be repeated multiple times, you can click the "Save" button at the bottom of the window to save the batch process to a text file. Click the "Open" button also at the bottom of the window to reload the same job. This feature automates the most redundant parts of your research so you can focus on the actual science.

Considering that the ImageJ homepage alone has over 500 plug-ins and over 300 macros available, for the sake of brevity I can only touch on the most basic topics in this short article. Fortunately, there are also many specialized tutorials available, and there is excellent documentation on the core of ImageJ on the project homepage. If you find this tool useful for research, there will also be plenty of information to guide you in your area of ​​expertise.

About the Author:

Joey Bernard has a background in physics and computer science. This comes in handy in his day job as a computing research consultant at the University of New Brunswick. He also teaches computational physics and parallel programming.

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