Spatiotemporal segmentation for validation of rolling leukocyte tracking data
Processing of bulk microscopy video data requires automated tracking of rolling leukocytes in the hundreds to compute a rolling velocity distribution, which is an indispensable descriptor in inflammation research and anti/pro-inflammatory drug testing. However, for any automated tracking method to be successful, an automated validation process must exist to accept or reject the output of tracking. In this paper, we propose an automated validation technique that first generates a spatiotemporal image from the cell locations output by a tracking method; then, it segments the spatiotemporal image to detect the presence or absence of a leukocyte by employing an edge-response filter followed by an active contour method. The proposed direction sensitive edge-response filter, the maximum absolute average directional derivative (MAADD), computes the magnitude of the mean directional derivative over an oriented line segment and chooses the maximum of all such values within a range of orientations of the line segment. Our validation experiments show that the proposed method is successful in 93% of the trials using manual tracking, in 83% using correlation tracking and in 84% using active contour tracking method.