Intensity quantile estimation and mapping—a novel algorithm for the correction of image non-uniformity bias in HCS data | Zendy

Ernest Lo | Zendy; Emmanuelle Soleilhac | Zendy; Anne Martinez | Zendy; Laurence Lafanéchère | Zendy; Robert Nadon | Zendy

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Intensity quantile estimation and mapping—a novel algorithm for the correction of image non-uniformity bias in HCS data

Author(s) -

Ernest Lo,

Emmanuelle Soleilhac,

Anne Martinez,

Laurence Lafanéchère,

Robert Nadon

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts491

Subject(s) - quantile , image (mathematics) , pixel , algorithm , computer science , matlab , intensity (physics) , function (biology) , image processing , field (mathematics) , artificial intelligence , pattern recognition (psychology) , statistics , mathematics , physics , optics , evolutionary biology , pure mathematics , biology , operating system

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.

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