TY - GEN
T1 - Cell segmentation and tracking using texture-adaptive snakes
AU - Wang, Xiaoxu
AU - He, Weijun
AU - Metaxas, Dimitris
AU - Mathew, Robin
AU - White, Eileen
PY - 2007
Y1 - 2007
N2 - Identifying cell trajectories is an important step in analyzing physiological events in computerized Video Time-lapse Microcopy. The large variety and transformation of cell shapes and cells' Brownian motion make cell tracking a challenge problem. In this paper we present a cell tracking system, implemented as a particle filter within texture-adaptive active contour formulations. The texture-adaptive weights on the external energy of the active contour model enables snakes to bypass internal psuedo-edges and stop on low-contrast cell boundaries. Using the texture of cells as observation model, we can track cells whose locations follow a multimodal distribution with a particle filter. This system is a novel combination of tracking algorithms and deformable models, and allows for the first time to automatically track non-fluorescence cellular microscopy images. The implemented tracker is tested on both normal and autophagy cell image sequences, to demonstrate the properties of cells in autophagy.
AB - Identifying cell trajectories is an important step in analyzing physiological events in computerized Video Time-lapse Microcopy. The large variety and transformation of cell shapes and cells' Brownian motion make cell tracking a challenge problem. In this paper we present a cell tracking system, implemented as a particle filter within texture-adaptive active contour formulations. The texture-adaptive weights on the external energy of the active contour model enables snakes to bypass internal psuedo-edges and stop on low-contrast cell boundaries. Using the texture of cells as observation model, we can track cells whose locations follow a multimodal distribution with a particle filter. This system is a novel combination of tracking algorithms and deformable models, and allows for the first time to automatically track non-fluorescence cellular microscopy images. The implemented tracker is tested on both normal and autophagy cell image sequences, to demonstrate the properties of cells in autophagy.
KW - Biomedical microscopy
KW - Image segmentation
KW - Image texture analysis
KW - Tracking
UR - http://www.scopus.com/inward/record.url?scp=36348944677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36348944677&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/ISBI.2007.356798
DO - https://doi.org/10.1109/ISBI.2007.356798
M3 - Conference contribution
SN - 1424406722
SN - 9781424406722
T3 - 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings
SP - 101
EP - 104
BT - 2007 4th IEEE International Symposium on Biomedical Imaging
T2 - 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Y2 - 12 April 2007 through 15 April 2007
ER -