Laser speckle blood flow imaging enhancement method

Abstract

The present invention provides a laser speckle blood flow imaging enhancement method. The laser speckle blood flow imaging enhancement method is configured to perform noise reduction processing and the repairing processing of the laser speckle blood flow imaging to enhance the sharpness of the laser speckle blood flow imaging. Because the laser speckle blood flow imaging enhancement method employs the BEEMD processing algorithm for the laser speckle blood flow imaging, the microvessel image is more clearly displayed and more details are reflected so as to reduce the influence of the vibration noise on the blood flow dispersed map; because the employed BEEMD algorithm can effectively avoid the information loss at the aspect of the image rank correlation of the original EEMD algorithm to retain the completer image information as much as possible prior to the noise reduction operation; and finally, because the employed BEEMD algorithm is far better than each two-dimensional empirical mode decomposition (BEMD) on the computation speed, so that the laser speckle blood flow imaging enhancement method has a positive effect on the real-time monitoring in vivo.

Description

technical field [0001] The present invention relates to an enhancement algorithm for laser speckle blood flow imaging, in particular to a method for enhancing laser speckle blood flow imaging based on a two-dimensional ensemble empirical mode decomposition (Bi-Dimensional Ensemble Empirical Mode Decomposition, BEEMD) algorithm, belonging to Biomedical signal processing technology field. Background technique [0002] Laser Speckle Contrast Imaging (LSCI) is a technology that obtains the velocity of particles by analyzing the scattering characteristics of moving particles to coherent laser light, and can also provide two-dimensional blood flow distribution images. The recorded data is processed by laser speckle contrast analysis to obtain a contrast image, which can reflect the velocity information of the blood flow, and can also reflect the distribution of microvessels through the image. Considering from the perspective of experiment and hardware equipment, laser speckle con...

AI Technical Summary

Problems solved by technology

For two-dimensional spatial data or images, we often regard it as a sequence combination of one-dimensional data sequences in the x or y direction. The original algorithm is to perform EEMD on each row (or column) of two-dimensional data (image) separately. processing, and then combine all the row (column) processing results, although it can decompose images of different levels, but because it treats each dimension vector of the two-dimensional signal as an independent process, the two-dimensional signal is ignored , so the results obtained by this method are hardly satisfactory

In addition, some researchers have proposed to use different two-dimensional empirical mode decomposition (BEMD) methods, such as the BEMD algorithm based on Delaunay triangulation and surface difference method. The biggest defect of this type of algorithm is that the logic is complex and the processing time is too long

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