JBiSE  Vol.4 No.8 , August 2011
Automatic DNA sequencing for electrophoresis gels using image processing algorithms
Abstract: DNA electrophoresis gel is an important biologically experimental technique and DNA sequencing can be defined by it. Traditionally, it is time consuming for biologists to exam the gel images by their eyes and often has human errors during the process. Therefore, automatic analysis of the gel image could provide more information that is usually ignored by human expert. However, basic tasks such as the identification of lanes in a gel image, easily done by human experts, emerge as problems that may be difficult to be executed automatically. In this paper, we design an automatic procedure to analyze DNA gel images using various image processing algorithms. Firstly, we employ an enhanced fuzzy c-means algorithm to extract the useful information from DNA gel images and exclude the undesired background. Then, Gaussian function is utilized to estimate the location of each lane of A, T, C, and G on the gels images automatically. Finally, the location of each band on the gel image can be detected accurately by tracing lanes, renewing lost bands, and eliminating repetitive bands.
Cite this paper: nullLee, J. , Huang, C. , Wang, N. and Lu, C. (2011) Automatic DNA sequencing for electrophoresis gels using image processing algorithms. Journal of Biomedical Science and Engineering, 4, 523-528. doi: 10.4236/jbise.2011.48067.

[1]   Griffiths, A.J.F., Miller, J.M. and Suzuki, D.T. (2000) An introduction to genetic analysis. WH Freeman & Co., New York.

[2]   Moore, S.M. (2000) Understanding human genome. IEEE Spectrum, 37, 33-35. doi:10.1109/6.880951

[3]   Patel, D. (1994) Gel electrophoresis: Essential data. Wiley, New York.

[4]   Umesh, P.S. and Flint, J. (2003) An efficient tool for genetic experiments: Agarose gel image analysis. Pattern Recognition, 36, 2453-2461.

[5]   Lim, Y.W. and Lee, S.U. (1990) On the color image seg-mentation algorithm based on the thresholding and the fuzzy c-means techniques. Pattern Recognition, 23, 935- 952. doi:10.1016/0031-3203(90)90103-R

[6]   Suckling, J., Sigmundsson, T., Greenwood, K. and Bull-more, E.T. (1999) A modified fuzzy clustering algorithm for operator independent brain tissue classification of dual echo MR images. Magnetic Resonance Imaging, 17, 1065-1076. doi:10.1016/S0730-725X(99)00055-7

[7]   Phillips, W.E., Velthuizen, R.P., Phuphanich, S., Hall, L.O., Clarke, L.P. and Silbiger, M.L. (1995) Application of fuzzy c-means segmentation technique for differentia-tion in MR images of a hemorrhagic glioblastoma multi- forme. Magnetic Resonance Imaging, 13, 277-290. doi:10.1016/0730-725X(94)00093-I