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Adaptive contrast enhancement of two‐dimensional electrophoretic protein gel images facilitates visualization, orientation and alignment
Author(s) -
Van Belle Werner,
Sjøholt Gry,
Ånensen Nina,
Høgda KjellArild,
Gjertsen Bjørn Tore
Publication year - 2006
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.200500925
Subject(s) - thresholding , artificial intelligence , orientation (vector space) , computer science , computer vision , contrast (vision) , pattern recognition (psychology) , materials science , image (mathematics) , mathematics , geometry
Abstract 2‐DE is a powerful technique to discriminate post‐translationally modified protein isoforms. However, all steps of 2‐DE preparation and gel‐staining may introduce unwanted artefacts, including inconsistent variation of background intensity over the entire 2‐DE gel image. Background intensity variations limit the accuracy of gel orientation, overlay alignment and spot detection methods. We present a compact and efficient denoising algorithm that adaptively enhances the image contrast and then, through thresholding and median filtering, removes the gray‐scale range covering the background. Applicability of the algorithm is demonstrated on immunoblots, isotope‐labeled gels, and protein‐stained gels. Validation is performed in contexts of (i) automatic gel orientation based on Hough transformation, (ii) overlay alignment based on cross correlation and (iii) spot detection. In gel stains with low background variability, e.g . Sypro Ruby, denoising will lower the spot detection sensitivity. In gel regions with high background levels denoising enhances spot detection. We propose that the denoising algorithm prepares images with high background for further automatic analysis, without requiring manual input on a gel‐to‐gel basis.