Abstract
In order to understand gene function in bacterial life cycles, time lapse bioimaging is applied in combination with different marker protocols in so called microfluidics chambers (i.e., a multi-well plate). In one experiment, a series of T images is recorded for one visual field, with a pixel resolution of 60 nm/px. Any (semi-)automatic analysis of the data is hampered by a strong image noise, low contrast and, last but not least, considerable irregular shifts during the acquisition. Image registration corrects such shifts enabling next steps of the analysis (e.g., feature extraction or tracking). Image alignment faces two obstacles in this microscopic context: (a) highly dynamic structural changes in the sample (i.e., colony growth) and (b) an individual data set-specific sample environment which makes the application of landmarks-based alignments almost impossible. We present a computational image registration solution, we refer to as ViCAR: (Vi)sual (C)ues based (A)daptive (R)egistration, for such microfluidics experiments, consisting of (1) the detection of particular polygons (outlined and segmented ones, referred to as visual cues), (2) the adaptive retrieval of three coordinates throughout different sets of frames, and finally (3) an image registration based on the relation of these points correcting both rotation and translation. We tested ViCAR with different data sets and have found that it provides an effective spatial alignment thereby paving the way to extract temporal features pertinent to each resulting bacterial colony. By using ViCAR, we achieved an image registration with 99.9% of image closeness, based on the average rmsd of 4.10−2 pixels, and superior results compared to a state of the art algorithm.
Highlights
With the advent of technologies that permit advances in microscopy, either for high-resolution data acquisition or automation of processes, the volume, and complexity of bioimage data has increased to the point that it is no longer feasible to retain relevant information without the use of a computer (Peng et al, 2012)
The acquisition, analysis, and interpretation of high-resolution time-lapse microscopy images, acquired in such experiments, triggers specific questions to algorithm development ranging from registration to visualization
It is inappropriate to use the Probabilistic Hough Transform (PHT) based approach since it requires a prerequisite of the image data
Summary
With the advent of technologies that permit advances in microscopy, either for high-resolution data acquisition or automation of processes, the volume, and complexity of bioimage data has increased to the point that it is no longer feasible to retain relevant information without the use of a computer (Peng et al, 2012). This investigation requires the extraction, and the visualization of quantitative cell-specific data at different time points (Wang et al, 2010; Klein et al, 2012; Tarnawski et al, 2013; Mekterovicet al., 2014). Recent studies focus on understanding phenotypic heterogeneity of isogenic bacteria (Ackermann, 2015). This entails investigating the history of a bacterial microcolony, i.e., Sinorhizobium meliloti, a soil bacterium, which is few micrometers long. The acquisition, analysis, and interpretation of high-resolution time-lapse microscopy images, acquired in such experiments, triggers specific questions to algorithm development ranging from registration ( referred to as image alignment in bioimage informatics) to visualization
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