Microvascular blood flow estimation in sublingual microcirculation videos based on a principal curve tracing algorithm

Microcirculatory perfusion is an important metric for diagnosing pathological conditions in patients. Capillary density and red blood cell (RBC) velocity provide a measure of tissue perfusion. Estimating RBC velocity is a challenging problem due to noisy video sequences, low contrast between the ves...

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Bibliographic Details
Published in:2012 IEEE International Workshop on Machine Learning for Signal Processing pp. 1 - 6
Main Authors: You, S., Ataer-Cansizoglu, E., Erdogmus, D., Massey, M., Shapiro, N.
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2012
Subjects:
Blood
Estimation
Microcirculation
Noise
Optical imaging
principal curve tracing
red blood cells
Tracking
velocity estimation
Video sequences
Videos
ISBN:1467310247, 9781467310246
ISSN:1551-2541
Online Access:Get full text
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Summary:Microcirculatory perfusion is an important metric for diagnosing pathological conditions in patients. Capillary density and red blood cell (RBC) velocity provide a measure of tissue perfusion. Estimating RBC velocity is a challenging problem due to noisy video sequences, low contrast between the vessels and the background, and thousands of RBCs moving rapidly through video sequences. Typically, physicians manually trace small blood vessels and visually estimate RBC velocities. The task is labor intensive, tedious, and time-consuming. In this paper, we present a novel application of a principal curve tracing algorithm to automatically track RBCs across video frames and estimate their velocity based on the displacements of RBCs between two consecutive frames. The proposed method is implemented in one sublingual microcirculation video of a healthy subject.
ISBN:1467310247
9781467310246
ISSN:1551-2541
DOI:10.1109/MLSP.2012.6349763