Automatic identification of regions of interest with application to the quantification of DNA damage in cells

Visual systems that have evolved in nature appear to exercise a mechanism that places emphasis upon areas in a scene without necessarily recognising objects that lie in those areas. This paper describes the application of a new model of visual attention to the automatic assessment of the degree of DNA damage in cultured human lung fibroblasts. The visual attention estimator measures the dissimilarity between neighbourhoods in the image giving higher visual attention values to neighbouring pixel configurations that do not match identical positional arrangements in other randomly selected neighbourhoods in the image. A set of tools has been implemented that processes images and produces corresponding arrays of attention values. Additional functionality has been added that provides a measure of DNA damage to images of treated lung cells affected by ultraviolet light. The unpredictability of the image attracts visual attention with the result that greater damage is reflected by higher attention values. Results are presented that indicate that the ranking provided by the visual attention estimates compare favourably with an ‘experts’ visual assessment of the degree of damage. Potentially, visual attention estimates may provide an alternative method of calculating the efficacy of genotoxins or modulators of DNA damage in treated human cells.