3D 형광이미지 분석을 위한 레인 검출 및 추적 알고리즘

Lane Detection and Tracking Algorithm for 3D Fluorescence Image Analysis

  • 이복주 (한국기술교육대학교 대학원 컴퓨터공학부) ;
  • 문혁 (한국기술교육대학교 대학원 컴퓨터공학부) ;
  • 최영규 (한국기술교육대학교 대학원 컴퓨터공학부)
  • Lee, Bok Ju (Korea University of Technology and Education, School of Computer Science and Engineering) ;
  • Moon, Hyuck (Korea University of Technology and Education, School of Computer Science and Engineering) ;
  • Choi, Young Kyu (Korea University of Technology and Education, School of Computer Science and Engineering)
  • 투고 : 2016.02.17
  • 심사 : 2016.03.23
  • 발행 : 2016.03.31


A new lane detection algorithm is proposed for the analysis of DNA fingerprints from a polymerase chain reaction (PCR) gel electrophoresis image. Although several research results have been previously reported, it is still challenging to extract lanes precisely from images having abrupt background brightness difference and bent lanes. We propose an edge based algorithm for calculating the average lane width and lane cycle. Our method adopts sub-pixel algorithm for extracting rising-edges and falling edges precisely and estimates the lane width and cycle by using k-means clustering algorithm. To handle the curved lanes, we partition the gel image into small portions, and track the lane centers in each partitioned image. 32 gel images including 534 lanes are used to evaluate the performance of our method. Experimental results show that our method is robust to images having background difference and bent lanes without any preprocessing.



  1. Machado, Alexei, et al. "An iterative algorithm for segmenting lanes in gel electrophoresis images." Computer Graphics and Image Processing, 1997. Proceedings., X Brazilian Symposium on. IEEE, (1997).
  2. Chang, Han-Beet, et al. "Field emission properties of carbon nanotubes grown on micro-tip substrates using an electrophoretic deposition method" Journal of the Semiconductor & Display Technology, 9(4), pp. 7-12, (2010)
  3. Hoelzel, A. Rus, and Gabriel A. Dover. "Molecular genetic ecology." IRL Press at Oxford University Press, (1991).
  4. Wong, Richard TF, et al. "LaneRuler: automated lanetracking for dna electrophoresis gel images." IEEE transactions on automation science and engineering,7.3, pp. 706-708, (2010). https://doi.org/10.1109/TASE.2009.2035437
  5. Kim, Taek Hyeon, et al. "Automatic DNA Image Recognition System for Diagnosis of Tuberculosis." Journal of Korea Multimedia society, 2009.5, pp. 722-725, (2009).
  6. Kim Seung Il, et al. "Automatic Alignment of Electrophoresis Gel Images Based on Standard Deviation of Vertical Profiles." Journal of KISS : Software and Applications, 39.8, pp. 631-638, (2012).
  7. Lee, Wan Yeon, et al. "Automatic Analysis Scheme for Multiple Images of Ongoing Electrophoresis Gel." Journal of KISS : Software and Applications, 39.8, pp. 672-677, (2012)
  8. Lee, Jiann-Der, et al. "Automatic DNA sequencing for electrophoresis gels using image processing algorithms." Journal of Biomedical Science and Engineering, 4.08, pp. 523, (2011). https://doi.org/10.4236/jbise.2011.48067
  9. Ye, Xiangyun, et al. "A recent development in image analysis of electrophoresis gels." Vision Interface'99, Trois-Rivieres, 19.21, pp. 432-438, (1999).
  10. Akbari, et al. "Automatic lane detection and separation in one dimensional gel images using continuous wavelet transform." Analytical Methods, 2.9, pp. 1360-1371, (2010). https://doi.org/10.1039/c0ay00167h
  11. Park, Sang Cheol, et al. "Lane detection and tracking in PCR gel electrophoresis images." Computers and electronics in agriculture, 83, pp. 85-91, (2012). https://doi.org/10.1016/j.compag.2012.01.016