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Blood Flow Velocity Detection of Nailfold Microcirculation Based on Spatiotemporal Analysis

机译:基于时空分析的锥形微循环血流速度检测

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摘要

Nailfold microcirculation can reflect the state of health. It is an important research topic to detect the change of microcirculation blood flow velocity. In order to save cost, people usually choose the low-cost micro camera to collect samples. However, the microvascular video collected by such camera is often disturbed by various noises, resulting in poor contrast and clarity of the image. Due to the large number of microvessels, it is time-consuming and laborious to detect the blood flow rate manually, which is inefficient and difficult to accurately detect the subtle changes of the blood flow velocity. At present, the previous research of blood flow velocity detection is mainly focused on the clearer microcirculation video. It is difficult to find any related research of blood flow velocity detection for noise image. For the low-quality nailfold microcirculation video collected by the low-cost microscope camera, we propose an automatic detection method of blood flow velocity based on projection analysis of spatiotemporal image. The method is as follows: firstly, video preprocessing, correlation matching are used to remove jitter and image blur is eliminated by deconvolution, the row mean is used to eliminate reflective area by analyzing the characteristics of noise distribution; secondly, we use cumulative background modeling to segment blood vessels and propose an automatic detection algorithm of blood vessel centerline; thirdly, the direction of binary spatiotemporal image is detected by using rotation projection, and then the blood flow velocity is calculated. The experimental results show that the proposed method can detect the blood flow velocity of microcirculation automatically and efficiently. Meanwhile, the average correlation coefficient between the proposed method and the manual measurement standard value is 0.935.
机译:甲襞微能反映健康的状态。这是一个重要的研究课题,以检测微循环血流速度的变化。为了节省成本,人们通常选择低成本的微型摄像头采集样品。然而,由这样的照相机收集的微血管视频通常是通过各种噪声干扰,从而导致图像的对比度差和清晰度。由于大量的微血管的,它是费时和费力的,以检测手动血液流速,这是低效的和困难的,以准确地检测出血流速度的细微变化。目前,血流速度检测以往的研究主要集中在更清晰的视频微循环。这是很难找到的血液流速检测的噪声图像的任何相关研究。由低成本的显微镜照相机收集到的低质量的甲襞微循环的视频中,我们提出基于时空图像的投影分析血流速度的自动检测方法。该方法如下:首先,视频预处理,相关匹配用于去除抖动和图像抖动是由去卷积消除,行平均被用于通过分析噪声分布的特性,以消除反射区域;其次,我们使用累积背景建模到段血管和提出血管中心线的自动检测算法;第三,二进制时空图像的方向是通过使用旋转投影检测,然后血流速度被计算。实验结果表明,所提出的方法能自动地和有效地检测微循环血流速度。同时,所提出的方法和手工测量的标准值之间的平均相关系数为0.935。

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