The observed motion of subcellular particles in fluorescence microscopy image sequences of live cells is generally
a superposition of the motion and deformation of the cell and the motion of the particles. Decoupling the two
types of movements to enable accurate classification of the particle motion requires the application of registration
algorithms. We have developed an intensity-based approach which is based on an optic-flow estimation algorithm
for non-rigid registration of multi-channel microscopy image sequences of cell nuclei. First, based on 3D synthetic
images we demonstrate that cell nucleus deformations change the observed motion types of particles and that
our approach allows to recover the original motion. Second, we have successfully applied our approach to register
2D and 3D real microscopy image sequences. A quantitative comparison with a previous scheme has also been
performed.
A bottleneck for high-throughput screening of live cells is the automated analysis of the generated image data.
An important application in this context is the evaluation of the duration of cell cycle phases from confocal time-lapse
microscopy image sequences, which typically involves a tracking step. The tracking step is an important
part since it relates segmented cells from one time frame to the next. However, a main problem is that often the
movement of single cells is superimposed with a global movement. The reason for the global movement lies in
the high-throughput acquisition of the images and the repositioning of the microscope. If a tracking algorithm
is applied to these images then only a superposition of the microscope movement and the cell movement is
determined but not the real movement of the cells. In addition, since the displacements are generally larger, it
is more difficult to determine the correspondences between cells. We have developed a phase-correlation based
approach to compensate for the global movement of the microscope by registering each image of a sequence to a
reference coordinate system. Our approach uses a windowing function in the spatial domain of the cross-power
spectrum. This allows to determine the global movement by direct evaluation of the phase gradient, avoiding
phase unwrapping. We present experimental results of applying our approach to synthetic and real image
sequences. It turns out that the global movement can well be compensated and thus successfully decouples the
global movement from the individual movement of the cells.
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