Abstract
Cell boundary segmentation in live cell image sequences is the first step towards quantitative analysis of cell motion and behavior. The time lapse microscopy imaging produces large volumes of image sequence collections which requires fast and robust automatic segmentation of cell boundaries to utilize further automated tools such as cell tracking to quantify and classify cell behavior. This paper presents a methodology that is based on utilizing the temporal context of the cell image sequences to accurately delineate the boundaries of non-homogeneous cells. A novel flux tensor-based detection of moving cells provides initial localization that is further refined by a multi-feature level set-based method using an efficient additive operator splitting scheme. The segmentation result is processed by a watershed-based algorithm to avoid merging boundaries of neighboring cells. By utilizing robust features, the level-set algorithm produces accurate segmentation for non-homogeneous cells with concave shapes and varying intensities.