Region and Contour Based Cell Cluster Segmentation Algorithm for In-Situ Microscopy

In this contribution a new algorithm is proposed for segmenting the image regions of the cell clusters present in a static image captured by an in-situ microscope inside of a bioreactor. A cell cluster is a group of one or more cells that are very close to each other, almost overlapping. The new algorithm combines a contour based segmentation approach with a region based segmentation approach. First, seeds are selected only in the background. To this end, image contours and the first and second moments of the pixels' intensity values in the background and in the cell clusters are evaluated. The moments are estimated from the histogram of the pixels' intensity values by applying a Maximum-Likelihood estimator. Following, the background region is extracted by region growing from the selected seeds. Finally, the segmented regions of the cell clusters are those image regions which do not belong to the previously extracted background region. Experimental results show an improvement of 33.33% in the reliability and an improvement of 55.1% in the accuracy of the cell cluster segmentation results.