The laser speckle contrast analysis (LASCA) is one of the most applicable tools in microcirculation studies. While the basic idea, as well as experimental setup for this method, are fairly simple, there is still the room for advancing of data processing algorithms. Specifically, the conventional realizations of LASCA method may limit the spatial and/or temporal resolution and thus fail in the detection of very small contrast objects since they based on the fixed-size rectangular sliding window function. We suggest an alternative data processing algorithm based on the usage of the Gaussian sliding filter for a sequential determination of both spatial and temporal parts of the speckle contrast. The suggested replacement of conventional box filter leads to the monotonic damping of high-frequency spectral components that results in a better elimination of ringing and aliasing effects in the spatio-temporal speckle contrast outputs. Additionally, we show that such sliding filtration increases robustness with respect to the processing of a sequence of nonstabilised images. We support this consideration with representative examples of processing both surrogate and real experimental data.
Keywords: biomedical image processing; gaussian filter; laser speckle contrast analysis.
© 2019 John Wiley & Sons, Ltd.