Measurement of blood flow velocity for in vivo video sequences with motion estimation methods

Yansong Liu; Eli Saber; Angela Glading; Maria Helguera

doi:10.1117/12.2043255

21 March 2014 Measurement of blood flow velocity for in vivo video sequences with motion estimation methods

Yansong Liu, Eli Saber, Angela Glading, Maria Helguera

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 90342W (2014) https://doi.org/10.1117/12.2043255
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Measurement of blood flow velocity for in vivo microscopic video is an invasive approach to study microcirculation systems, which has been applied in clinical analysis and physiological study. The video sequences investigated in this paper are recording the microcirculation in a rat brain using a CCD camera with a frame rate of 30 fps. To evaluate the accuracy and feasibility of applying motion estimation methods, we have compared both current optical flow and particle image velocimetry (PIV) techniques using cross-correlation by testing them with simulated vessel images and in vivo microscopic video sequences. The accuracy is evaluated by calculating the mean square root values of the results of these two methods based on ground truth. The limitations of applying both algorithms to our particular video sequences are discussed in terms of noise, the effect of large displacements, and vascular structures. The sources of erroneous motion vectors resulting from utilizing microscopic video with standard frame rate are addressed in this paper. Based on the above, a modified cross-correlation PIV technique called adaptive window cross-correlation (AWCC) is proposed to improve the performance of detecting motions in thinner and slightly complex vascular structures.

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Yansong Liu, Eli Saber, Angela Glading, and Maria Helguera "Measurement of blood flow velocity for in vivo video sequences with motion estimation methods", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90342W (21 March 2014); https://doi.org/10.1117/12.2043255

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KEYWORDS

Optical flow

Video

Motion estimation

Blood

Blood circulation

Particles

In vivo imaging

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