Introduction to Background Subtraction

In quantitative Western blotting, image background affects the accuracy and variability of analysis. Nonspecific bands, smears, spots, and blotches in Western blot images can make it difficult to precisely determine band intensity. When you draw a shape around a band, background pixels inside the shape also contribute to the signal intensity. During image analysis, background must be subtracted to accurately calculate the signal intensity for regions of interest. An algorithm estimates the intensity of background pixels and subtracts that value from the total signal intensity of the shape to “correct” for background.

Background subtraction is an important aspect of quantitative Western blot analysis – but its accuracy is strongly influenced by both image context and the type of subtraction algorithm applied (1-3). Currently available Western blot analysis software offers multiple options for subtracting background, and it can be difficult to know which method will produce the most accurate and reproducible quantification results. With little guidance about when and why each method is appropriate, researchers are forced to either pick a method or simply use the default settings and hope for the best. These subjective choices are based on each user’s personal preferences and introduce variation that makes the analysis less reproducible.

Background subtraction works best when image background is uniform and consistent across the blot, with well-separated bands and lanes (1, 4, 5). But every blot is different; electrophoresis artifacts, uneven background, smears, spots, and variations in lane or band spacing make each blot unique. Most background algorithms cannot adapt to these variations and will not produce accurate results every time (1, 2, 5). An ideal background subtraction algorithm for Western blot analysis would respond and adapt to these variations and produce accurate, reproducible results from every blot.

Common background subtraction algorithms rely on the shape or lane context. Shape-based methods use the area surrounding a shape or feature to estimate image background, and lane-based methods use information from the context of each lane. A new method that considers both the shape context and lane context, called Adaptive Background Subtraction (ABS), eliminates user bias for more reproducible and less subjective estimation of image background.