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Segmentation and tracking of live cells in phase‐contrast images using directional gradient vector flow for snakes
Author(s) -
SEROUSSI I.,
VEIKHERMAN D.,
OFER N.,
YEHUDAIRESHEFF S.,
KEREN K.
Publication year - 2012
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/j.1365-2818.2012.03624.x
Subject(s) - vector flow , active contour model , segmentation , computer vision , artificial intelligence , morphological gradient , vector field , tracking (education) , boundary (topology) , contrast (vision) , image segmentation , computer science , magnification , intensity (physics) , image gradient , biological system , mathematics , scale space segmentation , physics , optics , geometry , mathematical analysis , biology , image texture , psychology , pedagogy
Summary Cell shape is an important characteristic of the physiological state of a cell and is used as a primary read‐out of cell behaviour in various assays. Automated accurate segmentation of cells in microscopy images is hence of large practical importance in cell biology. We report a simple algorithm for automated cell segmentation in high‐magnification phase‐contrast images, which takes advantage of the characteristic directionality of the local image intensity gradient at cellular boundaries due to the ‘halo‐effect’. We employ a two‐step algorithm in which a gradient vector flow (GVF) field is first used to direct active contours to an approximate cell boundary. A directional GVF (DGVF) field is then calculated by considering only edges for which the image intensity gradient is directed outwards with respect to the approximate cell contour. Subsequently, the DGVF field is used to refine the cell contour, by directing active contours to edges with the desired gradient directionality. This method allows us to accurately segment cells in an image series, as well as follow the dynamics of cell shape over time in an automated fashion.