TY - JOUR
T1 - A confident scale-space shape representation framework for cell migration detection
AU - Zhang, K.
AU - Xiong, H.
AU - Zhou, X.
AU - Yang, L.
AU - Wang, Y. L.
AU - Wong, S. T.C.
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2008/9
Y1 - 2008/9
N2 - Automated segmentation of time-lapse images is a method to facilitate the understanding of the intricate biological progression, e.g. cancer cell migration. To address this problem, we introduce a shape representation enhancement over popular snake models in the context of confident scale-space such that a higher level of interpretation can hopefully be achieved. Our proposed system consists of a hierarchical analytic framework including feedback loops, self-adaptive and demand-adaptive adjustment, incorporating a steerable boundary detail term constraint based on multiscale B-spline interpolation. To minimize the noise interference inherited from microscopy acquisition, the coarse boundary derived from the initial segmentation with refined watershed line is coupled with microscopy compensation using the mean shift filtering. A progressive approximation is applied to achieve represented as a balance between a relief function of watershed algorithm and local minima concerning multiscale optimality, convergence and robust constraints. Experimental results show that the proposed method overcomes problems with spurious branches, arbitrary gaps, low contrast boundaries and low signal-to-noise ratio. The proposed system has the potential to serve as an automated data processing tool for cell migration applications.
AB - Automated segmentation of time-lapse images is a method to facilitate the understanding of the intricate biological progression, e.g. cancer cell migration. To address this problem, we introduce a shape representation enhancement over popular snake models in the context of confident scale-space such that a higher level of interpretation can hopefully be achieved. Our proposed system consists of a hierarchical analytic framework including feedback loops, self-adaptive and demand-adaptive adjustment, incorporating a steerable boundary detail term constraint based on multiscale B-spline interpolation. To minimize the noise interference inherited from microscopy acquisition, the coarse boundary derived from the initial segmentation with refined watershed line is coupled with microscopy compensation using the mean shift filtering. A progressive approximation is applied to achieve represented as a balance between a relief function of watershed algorithm and local minima concerning multiscale optimality, convergence and robust constraints. Experimental results show that the proposed method overcomes problems with spurious branches, arbitrary gaps, low contrast boundaries and low signal-to-noise ratio. The proposed system has the potential to serve as an automated data processing tool for cell migration applications.
KW - 3T3 cell
KW - Cellular image segmentation
KW - Mean shift filtering
KW - Multiscale detail detection
KW - Snake model
KW - Time-lapse microscopy
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U2 - 10.1111/j.1365-2818.2008.02050.x
DO - 10.1111/j.1365-2818.2008.02050.x
M3 - Article
C2 - 18754994
AN - SCOPUS:50649105749
VL - 231
SP - 395
EP - 407
JO - Journal of Microscopy
JF - Journal of Microscopy
SN - 0022-2720
IS - 3
ER -