RAMTaB: Robust Alignment of Multi-Tag Bioimages
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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. Co-location of the proteins is necessary to analyze the molecular structure of a sample at each point under observation. 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.
Methodology/Principal Findings: 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 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 the 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 fluorescent image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.
S.E.A. Raza, A. Humayun, S. Abouna, D. Epstein, T. Nattkemper, M. Khan, and N. Rajpoot "RAMTaB: Robust Alignment of Multi-Tag Bioimages," PLoS ONE 7(2):e30894, February 2012. [DOI]
Click here to download a sample stack generated by TIS.