Occluding convex image segmentation for E.Coli microscopy images

State-of-the-art flow-chamber technology enables us to closely monitor individual growth of thousands of bacterial cells simultaneously and across time. These experiments provide us with spatio-temporal greyscale images from the early stage of growth. Due to a large number of cells and time points involved automated image analysis covering noise removal, cell recognition and occluding image segmentation becomes essential. In this paper we focus on occluding image segmentation. A novel convex hull based method has been devised by the authors, which is compared with previously published algorithms through testing on real and simulated images. Results clearly show that our convex hull based segmentation algorithm works better than the ones based on curvature.