9 Color Images [D]  [D] This section is partially extracted from the MBF ImageJ online manual at http://www.macbiophotonics.ca/imagej/colour_image_processi.htm.

Pseudocolor Images

True Color Images

Color Spaces and Color Separation

Color spaces describe the gamut of colors that image-handling devices deal with. Because human vision is trichromatic, most color models represent colors by three values. Mathematically, these values (color components) form a three-dimensional space such as the RGB, HSB, CIE Lab or YUV color space.

Figure 3 Representation of an eight pixel color image in the RGB and HSB color spaces. The RGB color space maps the RGB color model to a cube with Red (R) values increasing along the x-axis, Green (G) along the y-axis and Blue (B) along the z-axis. In the HSB cylindrical coordinate system, the angle around the central vertical axis corresponds to Hue (H), the distance from the axis corresponds to Saturation (S), and the distance along the axis corresponds to Brightness (B). In both cases the origin holds the black color. The right panel shows the same image after brightness reduction, easily noted by the vertical displacement along the HSB cylinder. Images produced using Kai Uwe Barthel’s 3D Color Inspector plugin.

Conveying Color Information [F]  [F] This section is partially extracted from Masataka Okabe and Kei Ito, Color Universal Design (CUD) — How to make figures and presentations that are friendly to Colorblind people, http://jfly.iam.u-tokyo.ac.jp/color/, accessed 2009.01.15

Colorblind people can recognize a wide ranges of colors. But certain ranges of colors are hard to distinguish. The frequency of colorblindness is fairly high. One in 12 Caucasian (8%), one in 20 Asian (5%), and one in 25 African (4%) males are so-called ‘red--green’ colorblind.

There are always colorblind people among the audience and readers. There should be more than ten colorblind in a room with 250 people (assuming 50% male and 50% female).

[ …] There is a good chance that the paper you submit may go to colorblind reviewers. Supposing that your paper will be reviewed by three white males (which is not unlikely considering the current population in science), the probability that at least one of them is colorblind is whopping 22%!

Figure 4 Red--green images and partial color blindness. Deuteranopia (second panel), protanopia (third panel) are the most common types of partial color blindness (red / green confusion). Tritanopia (blue / orange confusion, fourth panel) is quite rare. Replacing Red with Magenta in RGB Images↓ (bottom row) is a simple way to compensate for color vision deficiencies.

One practical point defined by the Color Universal Design is the use of magenta in red--green overlays (see also [43]). Magenta is the equal mixture of red and blue. Colorblind people that have difficulties recognizing the red component can easily recognize the blue hue. The region of double positive becomes white, which is easily distinguishable for colorblind. In ImageJ this is easily accomplished using the Image▷Color▷Merge Channels…↓, or using the ImageJ macro language (see 5: Replacing Red with Magenta in RGB Images↓).

5 Replacing Red with Magenta in RGB Images

When building RGB images, magenta can be obtained using the Image▷Color▷Merge Channels…↓ Previously created RGB images can be converted to ‘MGB’ using Image▷Color▷Channels Tool… [Z]↓. Alternatively, the Process▷Image Calculator…↓ command can be used to add the red channel to the blue channel. Both these approaches can be automated using the ImageJ macro language as exemplified by Macros Replace Red with Magenta (1)↓ and Replace Red with Magenta (2)↓. Once saved in the ImageJ/plugins/ folder these Macros↓ are treated as regular ImageJ commands.

In Fiji↑, as expected, the procedure of modifying RGB images is simpler: one just needs to run Image▷Color▷Replace Red with Magenta. For even more convenience, Fiji provides an analogous command that replaces the system clipboard’s image with a magenta-green one.

 /* This macro replaces Red with Magenta in RGB images using Process>Image Calculator...  command. */
   if (bitDepth!=24)
       exit("This macro requires an RGB image");
 setBatchMode(true);
   title= getTitle();
   r= title+" (red)"; g= title+" (green)"; b= title+" (blue)";
   run("Split Channels");
   imageCalculator("Add", b, r);
   run("Merge Channels...", "red=&r green=&g blue=&b");
   rename(title + " (MGB)");
 setBatchMode(false);

Color Composite Images

1. Each channel is kept separate from the others and can be turned on and off using the ‘Channels’ tool (Image▷Color▷Channels Tool… [Z]↓). This feature allows, e.g., to perform measurements on a specific channel while visualizing multiple.
2. Channels can be 8, 16 or 32-bit and can be displayed with any lookup table
3. More than 3 channels can be merged or kept separate

 /* This macro replaces Red with Magenta in RGB images using the Image>Color>Channels... tool. */
   if (bitDepth!=24)         // Ignore non-RGB images
       exit("This macro requires an RGB image");
 setBatchMode(true);         // Enter ‘Batch’ mode
   title = getTitle();       // Retrieve the image title
   run("Make Composite");    // Run Image>Color>Make Composite
   run("Magenta");           // Run Image>Lookup Tables>Magenta on channel 1
   run("RGB Color");         // Run Image>Type>RGB Color
   rename(title + " (MGB)"); // Rename the image
 setBatchMode(false);        // Restore ‘GUI’ mode