2 Dimensional Electrophoresis Gel Registration Using Point Matching and Local Image-Based Refinement

Recently, proteomics research has become a large growth area in the biosciences. Studies involve differential analysis of large sets of 2-D Electrophoresis (2-DE) gels. We present a robust and accurate 2-DE gel alignment algorithm which combines point matching and local image-based refinement. The algorithm uses a novel combination of Euclidian, shape context, image and feature based attributes to produce a point distance measure. Correspondence is determined using this measure and is further improved using an iterative M-estimation approach, and shown to be robust in the presence of large image distortions. Local image-based refinement is shown to improve significantly alignment accuracy. The high accuracy and robustness of the resulting system indicates that it is a promising method for use in practical gel alignment situations.