Bias image correction via stationarity maximization.
Automated acquisitions in microscopy may come along with strong illumination artifacts due to poor physical imaging conditions. Such artifacts obviously have direct consequences on the efficiency of an image analysis algorithm and on the quantitative measures. In this paper, we propose a method to correct illumination artifacts on biological images. This correction is based on orthogonal polynomial modeling, combined with stationary maximization criteria. To validate the proposed method we show that we improve particle detection algorithm.
Dorval
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T
Image Mining Group, Institut Pasteur Korea, 39-1, Hawolgok-dong, Seongbuk-gu, Seoul 136-791, Korea.
Ogier
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Genovesio
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D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D008853:Microscopy / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes