Structural matching of 2D electrophoresis gels using deformed graphs

Alexandre Noma, Alvaro Pardo, Roberto M. Cesar

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

2D electrophoresis is a well-known method for protein separation which is extremely useful in the field of proteomics. Each spot in the image represents a protein accumulation and the goal is to perform a differential analysis between pairs of images to study changes in protein content. It is thus necessary to register two images by finding spot correspondences. Although it may seem a simple task, generally, the manual processing of this kind of images is very cumbersome, especially when strong variations between corresponding sets of spots are expected (e.g. strong non-linear deformations and outliers). In order to solve this problem, this paper proposes a new quadratic assignment formulation together with a correspondence estimation algorithm based on graph matching which takes into account the structural information between the detected spots. Each image is represented by a graph and the task is to find a maximum common subgraph. Successful experimental results using real data are presented, including an extensive comparative performance evaluation with ground-truth data.

Original languageEnglish
Pages (from-to)3-11
Number of pages9
JournalPattern Recognition Letters
Volume32
Issue number1
DOIs
StatePublished - 1 Jan 2011

Keywords

  • Deformed graphs
  • Electrophoresis gels
  • Graph matching
  • Maximum common subgraph
  • Point matching
  • Structural pattern recognition

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