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## Color allocation

The human visual system cannot see more than 60 shades of gray, which makes color management for gray-scale images much more simple than for color ones. Since we wanted to change the colors dynamically and use predefined colormaps, we chose to display the images using an 8-bit PseudoColor Display. This requirement is in our opinion fairly minimal. Besides, our experience proved that dithered images cannot be compared accurately.

In order to allow for fast array transformations, the colors are allocated from a contiguous set. For example, the palette can be inverted quickly by reversing the color indices. The images are displayed in false colors by choosing from a variety of prefined look-up tables. The histogram can be stretched or thresholded to produce a binary image. Furthermore, the colors can be allocated from shared, default or private colormaps. Changing the colors of one shared colormap will affect all the images that share it. This feature allows the user to visualize the same image displayed with different colors, or to compare the result of two different thresholds.

One additional benefit is that the color allocation can be used to display ``semi-transparent'' overlays. This is a feature that was found very useful in our interactive segmentation work. The user can for example ``paint'' on the image, draw polygons, Bezier or free-form curves, and yet guess the gray-level values. The overlays can be saved as a mask image. Let us point out that this feature enables the user to extract non-rectangular regions of interest. Alternatively, a mask image can be overlaid on top on the active image, in order to check an off-line segmentation over, remove spurious pixels or compare two data sets. The user can interactively combine overlays with logical operations (or, xor, and, etc..) by changing the Graphic Context. The pict widget provides an interface to advanced image processing routines [1, 2], and the overlays can also be used in semi-interactive segmentation [3], where a coarse initialization is specified; the segmented result can then be displayed. Examples in Figure 1(a)-(d) show two cross-sections of a CT volume displayed with different look-up tables. The concept of transparent overlays is described in Figure 2(a)-(c). The use of overlays in semi-automatic segmentation is presented in Figure 2(d)-(f).

Next: VISU: a volume VISUalization Up: The pict gray-scale image Previous: Data management