Tracking biological cells in time-lapse microscopy: an adaptive technique combining motion and topological features

This paper presents a vision-based method for automatic tracking of biological cells in time-lapse microscopy by combining the motion features with the topological features of the cells. The automation of tracking frequently faces problems of segmentation error and of finding correct cell correspondence in consecutive frames, since the cells are of varying size and shape, and may have uneven movement; these problems become more acute when the cell population is very high. To reduce the segmentation error, we introduce a cell-detection method based on h-maxima transformation, followed by the fitting of an ellipse for the nucleus shape. To find the correct correspondence between the detected cells, the topological features, namely, color compatibility, area overlap and deformation are combined with the motion features of skewness and displacement. This reduces the ambiguity of matching and constructs accurately the trajectories of the cell proliferation. Finally, a template-matching-based backward tracking procedure is employed to recover any break in a cell trajectory that may occur due to the segmentation errors or the presence of a mitosis. The tracking procedure is tested using a number of different cell sequences with nonuniform illumination, or uneven cell motion, and is shown to provide high accuracy both in the detection and the tracking of the cells.