Center for Data-Driven Science and Artificial Intelligence, Tohoku University, Sendai 980-8576, Japan.
Graduate School of Design and Architecture, Nagoya City University, Nagoya 467-8601, Japan.
Sensors (Basel). 2021 Sep 18;21(18):6256. doi: 10.3390/s21186256.
In this paper, we will introduce a method for observing microvascular waves (MVW) by extracting different images from the available images in the video taken with consumer cameras. Microvascular vasomotion is a dynamic phenomenon that can fluctuate over time for a variety of reasons and its sensing is used for variety of purposes. The special device, a side stream dark field camera (SDF camera) was developed in 2015 for the medical purpose to observe blood flow from above the epidermis. However, without using SDF cameras, smart signal processing can be combined with a consumer camera to analyze the global motion of microvascular vasomotion. MVW is a propagation pattern of microvascular vasomotions which reflects biological properties of vascular network. In addition, even without SDF cameras, MVW can be analyzed as a spatial and temporal pattern of microvascular vasomotion using a combination of advanced signal processing with consumer cameras. In this paper, we will demonstrate that such vascular movements and MVW can be observed using a consumer cameras. We also show a classification using it.
在本文中,我们将介绍一种通过从视频中可用的图像中提取不同的图像来观察微血管波(MVW)的方法。微血管运动是一种动态现象,由于各种原因,其波动会随时间而变化,其传感用于各种目的。特殊设备,即侧流暗场相机(SDF 相机)于 2015 年开发用于医疗目的,以观察表皮上方的血流。但是,即使不使用 SDF 相机,也可以将智能信号处理与消费级相机结合使用,以分析微血管运动的整体运动。MVW 是微血管运动的传播模式,反映了血管网络的生物学特性。此外,即使没有 SDF 相机,也可以使用消费级相机和先进的信号处理技术相结合,对微血管运动的时空模式进行分析。在本文中,我们将证明可以使用消费级相机观察到这种血管运动和 MVW。我们还展示了一种分类方法。