Automated spatial and temporal image analysis of bacterial cell growth

The state-of-the-art technology using ow-chamber microscopy imaging enables us to gain insight into the arcana of bacterial cell growth. However, a large number of high resolution develop- mental image data sets are produced that need to be properly processed and analyzed. The mathematical challenge lies in the automated image analysis, extraction of cell size profiles and determination of the time to cell division. Detailed noise analysis was carried out to correctly ?filter out the noise without losing important image information. A novel occluding convex image segmentation is developed which outperforms the existing algorithms in the literature. Next cell size parameters are identified via inertia equivalent ellipse ?fitting. Finally, individual cell division times are computed using k-means clustering. The information about individual cell division time distributions is of great value, as it has not been available before on such a large scale. Therefore this type of automation plays a key role in the new era of cell growth modelling