A method for three-dimensional motion analysis designed for live cell imaging by fluorescence confocal microscopy is described. The approach is based on optical flow computation and takes into account brightness variations in the image scene that are not due to motion, such as photobleaching or fluorescence variations that may reflect changes in cellular physiology. The 3-D optical flow algorithm allowed almost perfect motion estimation on noise-free artificial sequences, and performed with a relative error of <10% on noisy images typical of real experiments. The method was applied to a series of 3-D confocal image stacks from an in vitro preparation of the guinea pig cochlea. The complex motions caused by slow pressure changes in the cochlear compartments were quantified. At the surface of the hearing organ, the largest motion component was the transverse one (normal to the surface), but significant radial and longitudinal displacements were also present. The outer hair cell displayed larger radial motion at their basolateral membrane than at their apical surface. These movements reflect mechanical interactions between different cellular structures, which may be important for communicating sound-evoked vibrations to the sensory cells. A better understanding of these interactions is important for testing realistic models of cochlear mechanics.
A variety of image and signal processing
algorithms based on wavelet filtering tools have been developed during the last few decades, that are well adapted to the experimental variability typically encountered in live biological
microscopy. A number of processing tools are reviewed, that use wavelets for adaptive image restoration and for
motion or brightness variation analysis by optical flow computation. The usefulness of these tools for biological
imaging is illustrated in the context of the restoration of images of the inner ear and the analysis of cochlear
motion patterns in two and three dimensions. I also report on recent work that aims at capturing fluorescence
intensity changes associated with vesicle dynamics at synaptic zones of sensory hair cells. This latest application requires one to separate the intensity variations associated with the physiological process under study from
the variations caused by motion of the observed structures. A wavelet optical flow algorithm for doing this is
presented, and its effectiveness is demonstrated on artificial and experimental image sequences.
We describe a novel confocal image acquisition system capable of measuring the sound-evoked motion of the organ of Corti. The hearing organ is imaged with a standard laser scanning confocal microscope during sound stimulation. The exact temporal relation between each image pixel and the sound stimulus is quantified. The motion of the structures under study is obtained by fitting a Fourier series to the time dimension of a continuous sequence of acquired images. Previous versions of this acquisition system used a simple search to find pixels with similar phase values. The Fourier series approach permits substantially faster image acquisition with reduced noise levels and improved motion estimation. The system is validated by imaging various vibrating samples attached to a feedback-controlled piezoelectric translator. When using a rigid sample attached to the translator, the system is capable of measuring motion with peak-to-peak amplitudes smaller than 50 nm with an error below 20% at frequencies between 50 and 600 Hz. Examples of image sequences from the inner ear are given, along with detailed performance characteristics of the method.
Image restoration algorithms provide efficient tools for recovering part of the information lost in the imaging process of a microscope. We describe recent progress in the application of deconvolution to confocal microscopy. The point spread function of a Biorad-MRC1024 confocal microscope was measured under various imaging conditions, and used to process 3D-confocal images acquired in an intact preparation of the inner ear developed at Karolinska Institutet. Using these experiments we investigate the application of denoising methods based on wavelet analysis as a natural regularization of the deconvolution process. Within the Bayesian approach to image restoration, we compare wavelet denoising with the use of a maximum entropy constraint as another natural regularization method. Numerical experiments performed with test images show a clear advantage of the wavelet denoising approach, allowing to `cool down' the image with respect to the signal, while suppressing much of the fine-scale artifacts appearing during deconvolution due to the presence of noise, incomplete knowledge of the point spread function, or undersampling problems. We further describe a natural development of this approach, which consists of performing the Bayesian inference directly in the wavelet domain.
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