Image Processing on Linux
I've covered several scientific packages in this space that generate nice graphical representations of your data and work, but I've not gone in the other direction much. So in this article, I cover a popular image processing package called ImageJ. Specifically, I am looking at Fiji, an instance of ImageJ bundled with a set of plugins that are useful for scientific image processing.
The name Fiji is a recursive acronym, much like GNU. It stands for "Fiji Is Just ImageJ". ImageJ is a useful tool for analyzing images in scientific research—for example, you may use it for classifying tree types in a landscape from aerial photography. ImageJ can do that type categorization. It's built with a plugin architecture, and a very extensive collection of plugins is available to increase the available functionality.
The first step is to install ImageJ (or Fiji). Most distributions will have a package available for ImageJ. If you wish, you can install it that way and then install the individual plugins you need for your research. The other option is to install Fiji and get the most commonly used plugins at the same time. Unfortunately, most Linux distributions will not have a package available within their package repositories for Fiji. Luckily, however, an easy installation file is available from the main website. It's a simple zip file, containing a directory with all of the files required to run Fiji. When you first start it, you get only a small toolbar with a list of menu items (Figure 1).
Figure 1. You get a very minimal interface when you first start Fiji.
If you don't already have some images to use as you are learning to work with ImageJ, the Fiji installation includes several sample images. Click the File→Open Samples menu item for a dropdown list of sample images (Figure 2). These samples cover many of the potential tasks you might be interested in working on.
Figure 2. Several sample images are available that you can use as you learn how to work with ImageJ.
If you installed Fiji, rather than ImageJ alone, a large set of plugins already will be installed. The first one of note is the autoupdater plugin. This plugin checks the internet for updates to ImageJ, as well as the installed plugins, each time ImageJ is started.
All of the installed plugins are available under the Plugins menu item. Once you have installed a number of plugins, this list can become a bit unwieldy, so you may want to be judicious in your plugin selection. If you want to trigger the updates manually, click the Help→Update Fiji menu item to force the check and get a list of available updates (Figure 3).
Figure 3. You can force a manual check of what updates are available.
Now, what kind of work can you do with Fiji/ImageJ? One example is doing counts of objects within an image. You can load a sample by clicking File→Open Samples→Embryos.
Figure 4. With ImageJ, you can count objects within an image.
The first step is to set a scale to the image so you can tell ImageJ how to identify objects. First, select the line button on the toolbar and draw a line over the length of the scale legend on the image. You then can select Analyze→Set Scale, and it will set the number of pixels that the scale legend occupies (Figure 5). You can set the known distance to be 100 and the units to be "um".
Figure 5. For many image analysis tasks, you need to set a scale to the image.
The next step is to simplify the information within the image. Click Image→Type→8-bit to reduce the information to an 8-bit gray-scale image. To isolate the individual objects, click Process→Binary→Make Binary to threshold the image automatically (Figure 6).
Figure 6. There are tools to do automatic tasks like thresholding.
Before you can count the objects within the image, you need to remove artifacts like the scale legend. You can do that by using the rectangular selection tool to select it and then click Edit→Clear. Now you can analyze the image and see what objects are there.
Making sure that there are no areas selected in the image, click Analyze→Analyze Particles to pop up a window where you can select the minimum size, what results to display and what to show in the final image (Figure 7).
Figure 7. You can generate a reduced image with identified particles.
Figure 8 shows an overall look at what was discovered in the summary results window. There is also a detailed results window for each individual particle.
Figure 8. One of the output results includes a summary list of the particles identified.
Once you have an analysis worked out for a given image type, you often need to apply the exact same analysis to a series of images. This series may number into the thousands, so it's typically not something you will want to repeat manually for each image. In such cases, you can collect the required steps together into a macro so that they can be reapplied multiple times. Clicking Plugins→Macros→Record pops up a new window where all of your subsequent commands will be recorded. Once all of the steps are finished, you can save them as a macro file and rerun them on other images by clicking Plugins→Macros→Run.
If you have a very specific set of steps for your workflow, you simply can open the macro file and edit it by hand, as it is a simple text file. There is actually a complete macro language available to you to control the process that is being applied to your images more fully.
If you have a really large set of images that needs to be processed, however, this still might be too tedious for your workflow. In that case, go to Process→Batch→Macro to pop up a new window where you can set up your batch processing workflow (Figure 9).
Figure 9. You can run a macro on a batch of input image files with a single command.
From this window, you can select which macro file to apply, the source directory where the input images are located and the output directory where you want the output images to be written. You also can set the output file format and filter the list of images being used as input based on what the filename contains. Once everything is done, start the batch run by clicking the Process button at the bottom of the window.
If this is a workflow that will be repeated over time, you can save the batch process to a text file by clicking the Save button at the bottom of the window. You then can reload the same workflow by clicking the Open button, also at the bottom of the window. All of this functionality allows you to automate the most tedious parts of your research so you can focus on the actual science.
Considering that there are more than 500 plugins and more than 300 macros available from the main ImageJ website alone, it is an understatement that I've been able to touch on only the most basic of topics in this short article. Luckily, many domain-specific tutorials are available, along with the very good documentation for the core of ImageJ from the main project website. If you think this tool could be of use to your research, there is a wealth of information to guide you in your particular area of study.