The method evaluates pixel values in selected color channels and compares them with specified threshold ranges. A pixel is retained when it satisfies the chosen color criteria and excluded when it does not. This channel-based decision allows analysis to focus on visual signals associated with stained cells, fluorescent structures, tissue regions, colonies, or anatomical features.
RGB and HSV provide different ways to describe the same image colors. RGB separates red, green, and blue channel values, whereas HSV represents hue, saturation, and value. Choosing between them changes how threshold criteria are expressed. The appropriate representation depends on which color characteristics best distinguish the biological feature from the surrounding image.
Threshold values determine which pixels satisfy the selection criteria, so changing them can alter the included region and the excluded background. Thresholds may be defined by the user or determined computationally. Consistent criteria are important because the resulting mask directly affects measurements of area, intensity, shape, and abundance.
A binary mask converts the color-based selection into a form that separates included pixels from excluded pixels. This provides a basis for measuring the selected feature rather than evaluating the entire image indiscriminately. In biological research, the mask can support quantitative comparisons of region size, signal intensity, shape, or feature abundance.
Begin with an image containing the feature of interest, choose relevant color channels such as RGB or HSV, and define thresholds manually or computationally. Apply those criteria to assign pixels to the selected or excluded class, producing a binary mask. The mask can then be used for measurements and subsequent image-based comparisons.
It is useful when a biological feature has a distinguishable color signal within a more complex image. Applications described for this approach include isolating stained cells, fluorescent structures, tissue regions, colonies, and anatomical features. By converting these visual signals into measurable regions, the technique supports quantitative analysis in both microscopy and photography.