Lane detection and tracking in PCR gel electrophoresis images
Computers and Electronics in Agriculture
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For the purpose of PCR-RFLP analysis, as in the case of human papillomavirus (HPV) typing, quantitative information needs to be extracted from images resulting from one-dimensional gel electrophoresis by associating the image intensity with the concentration of biological material at the corresponding position on a gel matrix. However, the background intensity of the image stands in the way of quantifying this association. We propose a novel, efficient methodology for modeling the image background with a polynomial function and prove that this can benefit the extraction of accurate information from the lane intensity profile when modeled by a superposition of properly shaped parametric functions.