Methods Inf Med 2009; 48(04): 320-323
DOI: 10.3414/ME9229
Original Articles
Schattauer GmbH

Geometric Alignment of 2D Gel Electrophoresis Images

S. Wörz
1   University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Heidelberg, Germany
,
M.-L. Winz
1   University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Heidelberg, Germany
,
K. Rohr
1   University of Heidelberg, BIOQUANT, IPMB, and DKFZ Heidelberg, Department of Bioinformatics and Functional Genomics, Biomedical Computer Vision Group, Heidelberg, Germany
› Author Affiliations
Further Information

Publication History

05 June 2009

Publication Date:
17 January 2018 (online)

Summary

Objectives: 2D gel electrophoresis (2-DE) is the method of choice for analyzing protein expression in the field of proteomics, for example, comparing a reference with a test population. However, due to complex physical and chemical processes the locations of proteins generally vary in different 2-DE images. To cope with these variations, accurate geometric alignment of 2-DE images is important.

Methods: We introduce a new elastic registration approach for 2-DE images, which is based on an analytic solution of the Navier equation using Gaussian elastic body splines (GEBS). With this approach cross-effects in elastic deformations can be handled, which is important for the registration of 2-DE images. In addition, landmark correspondences can be included to aid the registration in regions which are difficult to register using intensity information alone.

Results: We have successfully applied our approach to register 2-DE gel images of different levels of complexity. In each case, gel images from a reference group are compared with a test group. To analyze the performance of our approach, we have carried out a quantitative evaluation of the registration results. Moreover, we have performed an experimental comparison with a previous elastic registration scheme.

Conclusions: From the results we found that our approach is well-suited for the registration of 2-DE gel images of different levels of complexity and it turned out that the approach is superior to a previous hybrid scheme. Moreover, our approach is well-suited in a fully automatic setting and the performance can further be improved when landmark correspondences are available.

 
  • References

  • 1 Dowsey AW, Dunn MJ, Yang G-Z. The role of bioinformatics in two-dimensional gel electrophoresis. Proteomics 2003; 3 (Suppl. 08) 1567-1596.
  • 2 Berth M, Moser FM, Kolbe M, Bernhardt J. The state of the art in the analysis of 2D gel electrophoresis images. Applied Microbiology Biotechnology 2007; 76 (Suppl. 06) 1223-1243.
  • 3 Zitova B, Flusser J. Image Registration Methods: A Survey. Image and Vision Computing 2003; 24: 977-1000.
  • 4 Efrat A, Hoffmann F, Kriegel K, Schultz C, Wenk C. Geometric algorithms for the analysis of 2D-electrophoresis gels. Journal of Computational Biology 2002; 9 (Suppl. 02) 299-316.
  • 5 Rogers M, Graham J. Robust and Accurate Registration of 2-D Electrophoresis Gels Using Point-Matching. IEEE Trans. on Image Processing 2007; 16 (Suppl. 03) 624-635.
  • 6 Smilansky Z. Automatic registration for images of two-dimensional protein gels. Electrophoresis 2001; 22: 1616-1626.
  • 7 Veeser S, Dunn MJ, Yang GZ. Multiresolution image registration for two-dimensional gel electrophoresis. Proteomics 2001; 1: 856-870.
  • 8 Rohr K, Cathier P, Wörz S. Elastic registration of electrophoresis images using intensity information and point landmarks. Pattern Recognition 2004; 37 (Suppl. 05) 1035-1048.
  • 9 Rogers M, Graham J, Tonge RP. 2D Electrophoresis Gel Registration Using Point Matching and Local Image-Based Refinement. In: Hoppe A. editor. Proc 15th British Machine Vision Conference (BMVC’04). Kingston, UK: BMVA Press; 2004
  • 10 Sorzano CÓS, Thévenaz P, Unser M. Elastic Registration of Biological Images Using Vector-Spline Regularization. IEEE Trans. on Biomedical Engineering 2005; 52 (Suppl. 04) 652-663.
  • 11 Wang X, Feng DD. Hybrid Registration for 2D Gel Protein Images. In: Proc 3rd Asia-Pacific Bioinformatics Conference Singapore. London: Imperial College Press; 2005
  • 12 Wörz S, Rohr K. Physics-based elastic registration using non-radial basis functions and including landmark localization uncertainties. Computer Vision and Image Understanding 2008; 111: 263-274.
  • 13 Kohlrausch J, Rohr K, Stiehl HS. A New Class of Elastic Body Splines for Nonrigid Registration of Medical Images. Journal of Mathematical Imaging and Vision 2005; 23 (Suppl. 03) 253-280.
  • 14 Thévenaz P, Ruttimann UE, Unser M. A Pyramid Approach to Subpixel Registration Based on Intensity. IEEE Trans. on Image Processing 1998; 7 (Suppl. 01) 27-41.
  • 15 Wörz S, Winz ML, Rohr K. Geometric Alignment of 2D Gel Electrophoresis Images. In: Tolxdorff T, Braun J, Deserno TM, Handels H, Horsch A, Meinzer H-P (eds.). Proc. Workshop Bildverarbeitung für die Medizin (BVM’08). Berlin, Germany: Springer Berlin; 2008. pp 97-101.