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 language | English |
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Pages (from-to) | 3-11 |
Number of pages | 9 |
Journal | Pattern Recognition Letters |
Volume | 32 |
Issue number | 1 |
DOIs | |
State | Published - 1 Jan 2011 |
Keywords
- Deformed graphs
- Electrophoresis gels
- Graph matching
- Maximum common subgraph
- Point matching
- Structural pattern recognition