Red Blood Cell Tracking Using Optical Flow Methods

Abstract

The investigation of microcirculation is a critical task in biomedical and physiological research. In order to monitor human’s condition and develop effective therapies of some diseases, the microcirculation information, such as flow velocity and vessel density, must be evaluated in a noninvasive manner. As one of the tasks of microcirculation investigation, automatic blood cell tracking presents an effective approach to estimate blood flow velocity. Currently, the most common method for blood cell tracking is based on spatiotemporal image analysis, which has lots of limitations, such as the diameter of microvesssels cannot be too larger than blood cells or tracers, cells or tracers should have fixed velocity, and it requires the image with high qualification. In this paper, we propose an optical flow method for automatic cell tracking. The key algorithm of the method is to align an image to its neighbors in a large image collection consisting of a variety of scenes. Considering the method cannot solve the problems in all cases of cell movement, another optical flow method, SIFT (Scale Invariant Feature Transform) flow, is also presented. The experimental results show that both methods can track the cells accurately. Optical flow is specially robust to the case where the velocity of cell is unstable, while SIFT flow works well when there are large displacement of 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.

In-vivo full-field measurement of microcirculatory blood flow velocity based on intelligent object identification

“…The pixel values of the curve were acquired from the video frames to generate the spatiotemporal image. We employed the VRBCT method for flow velocity measurement, which is more intuitive, 4,35,36 to evaluate the accuracy of the spatiotemporal diagram method. Using this method, the trajectory of the RBCs can be visualized directly from the video frames.…”

Pixel-Level and Robust Vibration Source Sensing in High-Frame-Rate Video Analysis

“…Many researchers have developed object tracking methods and systems that provide a visual representation to robustly describe the spatiotemporal characteristics of object appearance [2]. Object tracking methods using a global visual representation that reflects the global statistical characteristics of an image region to be tracked have been proposed on the basis of various global image features such as optical flows [3,4,5], color histograms [6,7,8], and texture histograms [9,10,11]. By encoding the object appearance information from the selected interest points in images, local-feature-based object tracking methods have also been proposed on the basis of local features such as scale invariant feature transform (SIFT) [12,13], Haar-like features [14,15], the histogram of oriented gradient (HOG) [16,17,18], and the local binary pattern (LBP) [19,20,21].…”

Automated heart rate estimation in fish embryo

“…We propose a single tool capable of analyzing these two different types of cycles. Various methods already exist for estimating frequencies based on motion: for instance tracking methods [4], [5], integrated box methods [6], measuring the amount of changing pixels between frames [7], and optical flow [8]. Tracking methods follow one or more elements of a sequence.…”