TY - GEN
T1 - Feature selection, matching, and evaluation for subcellular structure tracking
AU - Wen, Quan
AU - Gao, Jean
AU - Luby-Phelps, Kate
PY - 2006
Y1 - 2006
N2 - Understanding the motility of subcellular particles like organelles, vesicles, or mRNAs Is critical to understand how cells regulate delivery of specific proteins from the site of synthesis to the site of action. The goal of this paper is to present a framework of feature selection, matching, and evaluation for the segmentation and tracking of green fluorescent protein (GFP) labeled subcellular structures. To select stable and distinctive features for small-sized subcellular particles, a grid-based minimum variance (GMV) feature selection method Is proposed. To robustly keep tracking of the selected features, we propose a mean minimum to maximum ratio (MMMR) similarity measure for feature matching. In order to quantitatively evaluate the proposed methods, we define two evaluation criteria, feature convergence rate (FCVR) and feature consistence rate (FCSR), which conform with the proximity and similarity properties of Gestalt visual perception theory. Our technique was validated on real confocal video data with comparison to traditional feature selection and matching methods.
AB - Understanding the motility of subcellular particles like organelles, vesicles, or mRNAs Is critical to understand how cells regulate delivery of specific proteins from the site of synthesis to the site of action. The goal of this paper is to present a framework of feature selection, matching, and evaluation for the segmentation and tracking of green fluorescent protein (GFP) labeled subcellular structures. To select stable and distinctive features for small-sized subcellular particles, a grid-based minimum variance (GMV) feature selection method Is proposed. To robustly keep tracking of the selected features, we propose a mean minimum to maximum ratio (MMMR) similarity measure for feature matching. In order to quantitatively evaluate the proposed methods, we define two evaluation criteria, feature convergence rate (FCVR) and feature consistence rate (FCSR), which conform with the proximity and similarity properties of Gestalt visual perception theory. Our technique was validated on real confocal video data with comparison to traditional feature selection and matching methods.
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U2 - 10.1109/IEMBS.2006.259936
DO - 10.1109/IEMBS.2006.259936
M3 - Conference contribution
C2 - 17946154
AN - SCOPUS:34047172165
SN - 1424400325
SN - 9781424400324
T3 - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
SP - 3013
EP - 3016
BT - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
T2 - 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06
Y2 - 30 August 2006 through 3 September 2006
ER -