title: RAMTaB: robust alignment of multi-tag bioimages.
abstract: BACKGROUND
  In recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images.
  
  
  RESULTS
  We employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies.
  
  
  CONCLUSIONS
  For the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.
authors.affiliation: Department of Computer Science, University of Warwick, Coventry, United Kingdom.
authors.forename: Shan-e-Ahmed
authors.initials: SE
authors.lastname: Raza
authors.forename: Ahmad
authors.initials: A
authors.lastname: Humayun
authors.forename: Sylvie
authors.initials: S
authors.lastname: Abouna
authors.forename: Tim W
authors.initials: TW
authors.lastname: Nattkemper
authors.forename: David B A
authors.initials: DB
authors.lastname: Epstein
authors.forename: Michael
authors.initials: M
authors.lastname: Khan
authors.forename: Nasir M
authors.initials: NM
authors.lastname: Rajpoot
mesh_terms: D000465:Algorithms; D003110:Colonic Neoplasms / Q000473:pathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D011506:Proteins / Q000378:metabolism*; D012015:Reference Standards; D013997:Time Factors
chemical_list: D011506:Proteins