RAMTaB: robust alignment of multi-tag bioimages

• Published in: PloS one Vol. 7; no. 2; p. e30894
• Main Authors: Raza, Shan-e-Ahmed, Humayun, Ahmad, Abouna, Sylvie, Nattkemper, Tim W, Epstein, David B A, Khan, Michael, Rajpoot, Nasir M
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 08.02.2012; Public Library of Science (PLoS)
• Subjects: Acoustics; Algorithms; Alignment; Annotations; Biochemistry; Biology; Biomolecules; Biophysics; Colonic Neoplasms - pathology; Colorectal cancer; Computational Biology; Computer science; Engineering; Fluorescence; Humans; Image acquisition; Image contrast; Image Processing, Computer-Assisted - methods; Imaging techniques; Informatics; Laboratories; Life sciences; Localization; Medical imaging; Microscopy; Phase contrast; Physics; Prostate cancer; Proteins; Proteins - metabolism; Reference Standards; Registration; Robustness; Tags; Time Factors; Visual field; Visual fields; Visual perception; United Kingdom > UK; Georgia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0030894

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. 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. 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.