Cellular Neural Networks for Gold Immunochromatographic Strip Image Segmentation

Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. Comparing to the traditional qualitative or semi-quantitative method, a completely quantitative interpretation of the strips can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm. It is shown that the CNN offers a robust method for accurately segmenting the test and control lines via adaptively setting the threshold value, and therefore serves as a novel image methodology for the interpretation of GICS.