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Enhancement of DNA Gel Images

  • CT Munnirah Niesha Mohd ShafeeEmail author
  • Ahmad Khudzairi Khalid
  • Zarith Sofiah Othman
Conference paper

Abstract

The use of image enhancement is to process an image so that result is more suitable than the original image for specific application. The objective of enhancing the image is to improve the standard or the quality of the image from the original one. DNA gel image is one of the digital medical images prove to be corrupted by some degree of noise. It is due to the presence of corruption present in a transmission and an acquisition of many effects. This type of image need to be enhanced before it can be used for analysis or diagnose. This paper compares three different techniques of image enhancement which are used to enhance the DNA gel images namely Enhancement of DNA Gel Image using Thresholding, Shifting, and Filtering Techniques or Method 1, Enhancement of DNA Gel Image using Background Subtraction Technique or Method 2, and Enhancement of DNA Gel Image using Improved Background Subtraction Method or Method 3. The evaluation of the result is done based on the calculation result of Peak Signal to Noise Ratio (PSNR) value. The experimental results show that the third method of image enhancement is a better method to be applied as it shows a higher PSNR value compared to the other which means it improves the image better.

Keywords

DNA gel image Enhancement PSNR 

References

  1. 1.
    Bailey, D.G., Christie, B.C.: Processing of DNA and protein electrophoresis gels by image analysis. In: 2nd New Zealand Conference on Image and Vision Computing, August 1994, pp. 221–228. Massey University, New Zealand (1994)Google Scholar
  2. 2.
    Quelhas, P., Marcuzzo, M., Mendonça, A.M., Campilho, A.: Cell nuclei and cytoplasm joint segmentation using the sliding band filter. IEEE Trans. Med. Imaging 29(8), 1463–1473 (2010).  https://doi.org/10.1109/TMI.2010.2048253CrossRefGoogle Scholar
  3. 3.
    Park, S.C., Na, I.S., Kim, S.H., Lee, G.S., Oh, K.H., Kim, J.H., Han, T.H.: Lanes detection in PCR gel electrophoresis images. In: 2011 IEEE 11th International Conference on Computer and Information Technology (CIT), August 2011, pp. 306–313. IEEE (2011).  https://doi.org/10.1109/cit.2011.89
  4. 4.
    Lin, C.Y., Ching, Y.T., Yang, Y.L.: Automatic method to compare the lanes in gel electrophoresis images. IEEE Trans. Inf. Technol. Biomed. 11(2), 179–189 (2007)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Caridade, C.M.R., Margal, A.R.S., Mendonga, T., Pessoa, A.M., Pereira, S.: An automatic method to identify and extract information of DNA bands in Gel Electrophoresis Images. In: Engineering in Medicine and Biology Society, EMBC 2009. Annual International Conference of the IEEE, September 2009, pp. 1024–1027. IEEE (2009).  https://doi.org/10.1109/iembs.2009.5332462
  6. 6.
    Mao, H., Xie, M.: Lane detection based on Hough transform and endpoints classification. In: 2012 International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP), December 2012, pp. 125–127. IEEE (2012).  https://doi.org/10.1109/icwamtip.2012.6413455
  7. 7.
    Kaur, M., Sharma, R.: Restoration of medical images using denoising. Int. J. Sci. Emerg. Technol. Latest Trends 5(1), 35–38 (2013)Google Scholar
  8. 8.
    Sengar, R.S., Upadhyay, A.K., Singh, M.: Robust Pre-processing and Post-Processing Methods for 2D Gel Electrophoresis Images using Non-Separable Quincunx WaveletGoogle Scholar
  9. 9.
    Ye, X., Suen, C.Y., Cheriet, M., Wang, E.: A recent development in image analysis of electrophoresis gels. In: Vision Interface, May 1999, vol. 99, pp. 19–21 (1999)Google Scholar
  10. 10.
    Gutierrez, M.A.S.: On the use of distance maps in the analysis of 1D DNA gel images. In: 2009 International Conference on Digital Image Processing, March 2009, pp. 172–176. IEEE (2009).  https://doi.org/10.1109/icdip.2009.58
  11. 11.
    Kaabouch, N., Schultz, R.R., Milavetz, B.: An analysis system for DNA gel electrophoresis images based on automatic thresholding an enhancement. In: 2007 IEEE International Conference on Electro/Information Technology, May 2007, pp. 26–31. IEEE (2007).  https://doi.org/10.1109/eit.2007.4374496
  12. 12.
    Goel, S., Verma, A., Kumar, N.: Gray level enhancement to emphasize less dynamic region within image using genetic algorithm. In: 2013 IEEE 3rd International Advance Computing Conference (IACC), February 2013, pp. 1171–1176. IEEE (2013)Google Scholar
  13. 13.
    Helmy, A.K., El-Tawel, G.S.: Semiautomatic detection of lanes and bands in DNA gel electrophoresis images. J. Biomed. Sci. Eng. 6(01), 76 (2013).  https://doi.org/10.4236/jbise.2013.61010CrossRefGoogle Scholar
  14. 14.
    Rashwan, S., Sarhan, A., Faheem, M.T., Youssef, B.A.: Fuzzy watershed segmentation algorithm: an enhanced algorithm for 2D gel electrophoresis image segmentation. Int. J. Data Min. Bioinform. 12(3), 275–293 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • CT Munnirah Niesha Mohd Shafee
    • 1
    Email author
  • Ahmad Khudzairi Khalid
    • 2
  • Zarith Sofiah Othman
    • 2
  1. 1.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARA Cawangan JohorSegamatMalaysia
  2. 2.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARA Cawangan JohorMasaiMalaysia

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