An improved method for velocity estimation of red blood cell in microcirculation

Lin, Wen-Chen; Lin, Tung-Ju; Tsai, Cheng-Lun; Lin, Kang‐Ping

doi:10.1109/embc.2014.6943567

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…The velocity of moving blood cells can also be measured by estimating the slope of the spacetime diagram of the light intensity changes in a line selected along a blood vessel [32]; however, this also results in the sacrifice of spatial resolution. To improve the spatial resolution of PIV, optical flow analysis is introduced, to trace the blood-flow velocity [33][34][35][36][37][38]. Given the assumption of constancy and similarity in the local brightness patterns in classic optical flow theory, the variation in the pixel brightness between consecutive frames is used to calculate the direction and speed of the moving blood cells.…”

Section: Introductionmentioning

confidence: 99%

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

Meng

Huang

Feng

et al. 2023

IJMS

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The quantitative measurement of the microvascular blood-flow velocity is critical to the early diagnosis of microvascular dysfunction, yet there are several challenges with the current quantitative flow velocity imaging techniques for the microvasculature. Optical flow analysis allows for the quantitative imaging of the blood-flow velocity with a high spatial resolution, using the variation in pixel brightness between consecutive frames to trace the motion of red blood cells. However, the traditional optical flow algorithm usually suffers from strong noise from the background tissue, and a significant underestimation of the blood-flow speed in blood vessels, due to the errors in detecting the feature points in optical images. Here, we propose a temporal direction filtering and peak interpolation optical flow method (TPIOF) to suppress the background noise, and improve the accuracy of the blood-flow velocity estimation. In vitro phantom experiments and in vivo animal experiments were performed to validate the improvements in our new method.

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Section: Introductionmentioning

confidence: 99%

Optical Flow-Based Full-Field Quantitative Blood-Flow Velocimetry Using Temporal Direction Filtering and Peak Interpolation

Meng

Huang

Feng

et al. 2023

IJMS

View full text Add to dashboard Cite

show abstract

“…For this reason, much research is ongoing with the aim to provide an automated assessment of microcirculatory parameters but, up to now, no established method is available (Massey and Shapiro 2016). Among the possible strategies, the most recently proposed methods include dynamic time warping (Grisan et al 2009), optical flow estimation (Liu et al 2015), and block matching algorithms (Lin et al 2014).…”

Section: Introductionmentioning

confidence: 99%