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Article | Open Access

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

Beibei Hu1, Xueqing Zhang2, Haopeng Chen1( ), Kan Wang2
School of Software
Department of Bio-Nano-Science and Engineering, National Key Laboratory of Nano/Micro Fabrication Technology, Key Laboratory for Thin Film and Microfabrication of Ministry of Education, Institute of Micro-Nano Science and Technology, Shanghai JiaoTong University, 800 Dongchuan Road, Shanghai 200240, China
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Abstract

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.

Keywords

Image segmentationChromatographic chipFluorescent signalPyramid connectionGMM

References

1

Arai H, Petchclai B, Khupulsup K, Kurimura T, Takeda K. Evaluation of a rapid immunochromatographic test for detection of antibodies to human immunodeficiency virus. J Clin Microbiol 1999; 37: 360-70.
Crossref Google Scholar
2

Overturf LA, Comer ML, Delp EJ. Color image coding using morphological pyramid decomposition. IEEE Trans. Image Process. 1995; 4(2): 177-185.doi:10.1109/83.342191
Crossref Google Scholar
3

Trahanias P E, Venetsanopolous AN. Vector directional filters-a new class of multichannel image processing filters. IEEE Trans. Image Process. 1993; 2(4): 528-534.doi:10.1109/83.242362
Crossref Google Scholar
4

Soille P. Morphological Image Analysis. Berlin, Germany: Springer-Verlag, 1999.
Crossref
5

Bister M, Cornelis J, Rosenfeld A. A critical view of pyramid segmentation algorithms. Pattern Recog. Lett. 1990; 11: 605–617.doi:10.1016/0167-8655(90)90013-R
Crossref Google Scholar
6

Prewer D, Kitchen L. Soft image segmentation by weighted linked pyramid. Pattern Recog. Lett. 2001; 22: 123–132.doi:10.1016/S0167-8655(00)00063-5
Crossref Google Scholar
7

Zivkovic Z. Improved adaptive gausian mixture model for background subtraction. Pattern Recognition, 2004; 2: 28–31.
Crossref Google Scholar
8

Stauffer C, Grimson W. Adaptive background mixture models for real-time tracking. Proceedings CVPR 1999; 31: 246–252.
Google Scholar
9

Harville M. A framework for high-level feedback to adaptive,per-pixel,mixture-of-gaussian background models. Proceedings of ECCV, 2002;2052:543-560.
Crossref Google Scholar
10

Wang X, Ghosh S, Guo SW. "Quantitative quality control in microarray image processing and data acquisition,"Nucleic Acids Research, 2001; 29: 75doi:10.1093/nar/29.15.e75
Crossref Google Scholar
11

Jain A. Fully automatic quantification of microarray image data. Genome Res, 2002; 12(2): 325-332.doi:10.1101/gr.210902
Crossref Google Scholar
12
Robert HJ, Junior BRF, Hashimoto. Microarray gridding by mathematical morphology[A]. Proceedings of XIV Brazilian Symposium on Computer Graphics and Image Processing. Sao Paulo, Brazil; IEEE, 2001: 112-119.
13

Goutsias J. Non Linear Multiresolution Signal Decomposition Schemes-Part I: Morphological Pyramids. IEEE Trans on Image Processing, 2000; 9(11):1862-1876. doi:10.1109/83.877209
Crossref Google Scholar
14

Xiang RH, Wang RS. A range image segmentation algorithm based on Gaussian mixture model. Journal of Software,2003; 14(7):1250-1257.
Google Scholar
15
Rother C, Blake A, Kolmogorov V. Grabcut-interactive foreground extraction using iterated graph cuts. In Proceedings of ACM SIGGRAPH 2004.
Crossref
16

Jahne B. Digital Image Processing. New York: Springer, 1997.
Crossref
17

Withagen PJ, Schutte K, Groen F. Likelihood-based object tracking using color histograms and EM.Proceedings ICIP, USA, 2002; 589–592.
Google Scholar
18

Elgammal A, Harwood D, Davis LS. Non-parametric background model for background subtraction. ECCV, 2000;1843:751-767.
Crossref Google Scholar
Nano Biomedicine and Engineering
Volume 2 Issue 3,
September 2010
Pages 177-181
DOI: 10.5101/nbe.v2i3.p177-181
Cite this article:
Hu B, Zhang X, Chen H, et al. Fluorescent Signal Detection of Immunochromatographic Chip Based on Pyramid Connection and Gaussian Mixture Model. Nano Biomedicine and Engineering, 2010, 2(3): 177-181. https://doi.org/10.5101/nbe.v2i3.p177-181

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Received: 20 July 2010
Accepted: 20 September 2010
Published: 01 October 2010
© 2010 B. Hu et al.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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