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Laser speckle contrast analysis (LASCA): a real-time solution for monitoring capillary blood flow and velocity

机译:激光散斑对比分析(LASCA):用于监测毛细血管血流和速度的实时解决方案

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Abstract: To realize a non-contact, non-invasive and fast measurement of skin blood flow, we have developed the laser speckle contrast analysis (LASCA) technique. The LASCA method is a spatial domain method, based on the aggregate of pixels composing a captured laser speckle image. The contrast calculation operates directly on these pixels. In this paper, we present the computer algorithms to achieve a real-time solution for monitoring capillary blood flow and velocity. First, we present an improved naive LASCA algorithm with the running time O(k$+2$), where n is the total number of pixels and k $MUL k represents the size of the subimage considered. Then, we describe a fast LASCA algorithm, which takes time O(kn) to calculate the local contrasts. Finally, we use the fast sequential algorithm to design the first LASCA parallel algorithm to run on the CREW PRAM with the running time O(k/p n), where p is the number of processors. Experimental result shows that, to process a laser speckle image with the size of 640 $MUL 480, it takes only about one second using the fast LASCA algorithm. Furthermore, our parallel algorithm is easily implemented to run under the Windows NT environment by using multi-threads technique. !14
机译:摘要:为了实现非接触,非侵入性和快速的皮肤血流量测量,我们开发了激光散斑对比度分析(LASCA)技术。 LASCA方法是一种空间域方法,基于构成捕获的激光斑点图像的像素的集合。对比度计算直接在这些像素上进行。在本文中,我们介绍了用于实现实时解决方案的计算机算法,以监控毛细血管的血流和流速。首先,我们提出了一种改进的朴素的LASCA算法,其运行时间为O(k $ + 2 $ / n),其中n是像素总数,k $ MUL k表示所考虑的子图像的大小。然后,我们描述了一种快速的LASCA算法,该算法需要时间O(kn)来计算局部对比度。最后,我们使用快速顺序算法设计第一个LASCA并行算法,以运行时间O(k / p n)在CREW PRAM上运行,其中p是处理器数量。实验结果表明,要处理大小为640 $ MUL 480的激光散斑图像,使用快速LASCA算法仅需大约一秒钟。此外,通过使用多线程技术,我们的并行算法很容易实现以在Windows NT环境下运行。 !14

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