Red Blood Cell Tracking Using Optical Flow Methods
- 16 September 2013
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Biomedical and Health Informatics
- Vol. 18 (3), 991-998
- https://doi.org/10.1109/jbhi.2013.2281915
Abstract
The investigation of microcirculation is an important task in biomedical and physiological research because the microcirculation information, such as flow velocity and vessel density, is critical to monitor human conditions and develop effective therapies of some diseases. As one of the tasks of the microcirculation study, red blood cell (RBC) tracking presents an effective approach to estimate some parameters in microcirculation. The common method for RBC tracking is based on spatiotemporal image analysis, which requires the image to have high qualification and cells should have fixed velocity. Besides, for in vivo cell tracking, cells may disappear in some frames, image series may have spatial and temporal distortions, and vessel distribution can be complex, which increase the difficulties of RBC tracking. In this paper, we propose an optical flow method to track RBCs. It attempts to describe the local motion for each visible point in the frames using a local displacement vector field. We utilize it to calculate the displacement of a cell in two adjacent frames. Additionally, another optical flow-based method, scale invariant feature transform (SIFT) flow, is also presented. The experimental results show that optical flow is quite robust to the case where the velocity of cell is unstable, while SIFT flow works well when there is a large displacement of the cell between two adjacent frames. Our proposed methods outperform other methods when doing in vivo cell tracking, which can be used to estimate the blood flow directly and help to evaluate other parameters in microcirculation.Funding Information
- NIH (U01HL111560-01)
- NIH (1U01CA166886-01)
- NIH (1R01DE022676-01)
- NIH/NCI Physical Sciences Oncology Center (PS-OC) Pilot Project Award to AV and ML as part of the Methodist (U54CA143837) PS-OC Center
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