Fluorescent Signal Detection of Immunochromatographic Chip Based on Pyramid Connection and Gaussian Mixture Model

Beibei Hu, Xueqing Zhang, Haopeng Chen, Kan Wang


The detection of fluorescent signal in chromatographic chip is the key of disease diagnosis by immunochromatographic assay. We propose a new algorithm for the automatic identification of fluorescent signal. Based on the features of chromatographic chips, mathematic morphology in RGB color space is used to filter and enhance the images, pyramid connection is used to segment the areas of fluorescent signal and then the method of Gaussian Mixture Model is available to detect the fluorescent signal. At last we calculate the average fluorescence intensity in obtained fluorescent areas. It can be proved that the algorithm has a good effect to segment the fluorescent areas and it can detect the fluorescent signal quickly and accurately to achieve the quantitative detection of chromatographic chip through experimental data analysis.

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Nano Biomedicine and Engineering.

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