KEYWORDS: Image segmentation, Denoising, Optical flow, Luminescence, Proteins, Wavelets, In vitro testing, 3D image processing, Signal to noise ratio, Image processing algorithms and systems
We present an approach for the computation of single-object velocity statistics in a noisy fluorescence image series. The algorithm is applied to molecular imaging data from an in vitro actin-myosin motility assay. We compare the relative efficiency of wavelet and curvelet transform denoising in terms of noise reduction and object restoration. It is shown that while both algorithms reduce background noise efficiently, curvelet denoising restores the curved edges of actin filaments more reliably. Noncrossing spatiotemporal actin trajectories are unambiguously identified using a novel segmentation scheme that locally combines the information of 2-D and 3-D segmentation. Finally, the optical flow vector field for the image sequence is computed via the 3-D structure tensor and mapped to the segmented trajectories. Using single-trajectory statistics, the global velocity distribution extracted from an image sequence is decomposed into the contributions of individual trajectories. The technique is further used to analyze the distribution of the x and y components of the velocity vectors separately, and it is shown that directed actin motion is found in myosin extracts from single skeletal muscle fibers. The presented approach may prove helpful to identify actin filament subpopulations and to analyze actin-myosin interaction kinetics under biochemical regulation.
KEYWORDS: Image segmentation, Denoising, Molecules, Signal to noise ratio, Image processing, Wavelets, Luminescence, Image processing algorithms and systems, Digital filtering, 3D image processing
We present a multiresolution transform-based method for the extraction of moving filament trajectories from single
molecule motility data. Noise-corrupted fluorescence image series are denoised using the multiscale median transform
and trajectories are detected in the denoised data set. The presented method reduces noise more efficiently than 2D-anisotropic
diffusion and several wavelet based techniques. Fibre trajectories are extracted by segmentation of the
denoised image stacks and non-crossing trajectories are unambiguously identified combining the information of 2D (XY)
and 3D (XYT) segmentation.
The algorithm is applied and evaluated using experimental data sets - image sequences of fluorescently labeled F-actin
molecules and their 2D-trajectories on a myosin coated surface. This so-called 'motility assay' is used to analyse
kinetics, biochemical regulation and pharmacological modulation of these biologically relevant molecules. The presented
method improves signal-to-background discrimination, facilitates filament identification and finally, may contribute to
significantly improve the performance of this assay.
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