title: Robust features for 2-DE gel image registration.
abstract: Proteomics is a rapidly growing field of modern biology. Since quantitative data of proteins involved in dynamic processes of living organisms are essential for understanding the basics of life, techniques like 2-DE and related procedures for automatic data interpretation are at the heart of this research field. They are strongly required to enable analysis and interpretation of the emerging amount of available data. Analyzing and interpreting gel image data usually requires the comparison of gels from different experiments and, thus, a prior registration of gels. This can be accomplished using featureless, feature-based or hybrid registration approaches combining both techniques. Recently, the latter ones have shown high performance, and it is undoubtful that in general robust and reliable features are an essential ingredient and valuable source of information for high-quality image registration. In this paper we provide a thorough overview and elaborate analysis of the capabilities of available feature detectors for gel image registration. Particularly, a detailed and extensive comparative study is presented where common spot-specific detectors are included as well as image-content independent detectors that were not applied to the task of gel image registration until now. The study incorporates tests on several thousand synthetically deformed images from different experimental conditions. As a result it provides valuable quantitative data allowing for direct objective comparisons of various detectors, and is well suited to guide the design of new registration algorithms.
authors: Institute of Computer Science, Martin-Luther-University Halle-Wittenberg, 06120 Halle, Germany. birgit.moeller@informatik.uni-halle.de
authors: Birgit
authors: B
authors: Möller
authors: Stefan
authors: S
authors: Posch
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D015180:Electrophoresis, Gel, Two-Dimensional / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results