Mosaicing of optical microscope imagery based on visual information

Abstract

Tools for high-throughput high-content image analysis can simplify and expedite different stages of biological experiments, by processing and combining different information taken at different time and in different areas of the culture. Among the most important in this field, image mosaicing methods provide the researcher with a global view of the biological sample in a unique image. Current approaches rely on known motorized x-y stage offsets and work in batch mode, thus jeopardizing the interaction between the microscopic system and the researcher during the investigation of the cell culture. In this work we present an approach for mosaicing of optical microscope imagery, based on local image registration and exploiting visual information only. To our knowledge, this is the first approach suitable to work on-line with non-motorized microscopes. To assess our method, the quality of resulting mosaics is quantitatively evaluated through on-purpose image metrics. Experimental results show the importance of model selection issues and confirm the soundness of our approach.

Improving reliability of live/dead cell counting through automated image mosaicing

“…The method we propose is based on a "Frame-To-Frame" registration strategy [29] and it is able to register image at pixel accuracy, yielding a seamless mosaic thanks to compensation of colours inhomogeneities in the stitching regions. GridMos is conceived to work off-line with phase-contrast or brightfield images, also acquired with no motorized widefield microscopes.…”

“…Images in histology and cytology are normally obtained by using a light widefield microscope [28], which is not typically endowed with motorized stage [29]. Accordingly, several mosaicing software tools are typically used to obtain a mosaic of a set of overlapping images manually acquired.…”

Vignetting and photo-bleaching correction in automated fluorescence microscopy from an array of overlapping images

“…Vignetting is caused by a number of sources [1,2], all of which can introduce errors in quantitative analyses or cause unwanted seams to appear in stitched image mosaics [3].…”

Real-time whole slide mosaicing for non-automated microscopes in histopathology analysis

“…Other authors propose an image mosaicing method for non automated microscopes relying on KLT (Kanade-Lucas-Tomasi) feature tracker to infer the transformation between each pair of images. [910] This approach is necessary when external information regarding the stage position is unavailable: The overlapped region between two consecutive images could occur at any point. However, such methods alone do not offer sufficient robustness in image microscopy, due to the presence of self similar regions in low-contrast images with few corner points, especially at a high magnification.…”