Mosaicing of optical microscope imagery based on visual information.

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.