Motivation: Image non-uniformity (NU) refers to systematic, slowly varying spatial gradients in images that result in a bias that can affect all downstream image processing, quantification and statistical analysis steps. Image NU is poorly modeled in the field of high-content screening (HCS), however, such that current conventional correction algorithms may be either inappropriate for HCS or fail to take advantage of the information available in HCS image data. Results: A novel image NU bias correction algorithm, termed intensity quantile estimation and mapping (IQEM), is described. The algorithm estimates the full non-linear form of the image NU bias by mapping pixel intensities to a reference intensity quantile function. IQEM accounts for the variation in NU bias over broad cell intensity ranges and data acquisition times, both of which are characteristic of HCS image datasets. Validation of the method, using simulated and HCS microtubule polymerization screen images, is presented. Two requirements of IQEM are that the dataset consists of large numbers of images acquired under identical conditions and that cells are distributed with no within-image spatial preference. Availability and implementation: MATLAB function files are available at http://nadon-mugqic.mcgill.ca/. Contact: robert.nadon@mcgill.ca Supplementary Information: Supplementary data are available at Bioinformatics online.
['Ernest Lo', 'Emmanuelle Soleilhac', 'Anne Martinez', 'Laurence Lafanechère', 'Robert Nadon']
Intensity quantile estimation and mapping—a novel algorithm for the correction of image non-uniformity bias in HCS data