The study on the parallel processing based time series correlation analysis of RBC membrane flickering in quantitative phase imaging

Minsuk Lee; Youngjae Won; Byungjun Park; Seungrag Lee

doi:10.1117/12.2253458

21 February 2017 The study on the parallel processing based time series correlation analysis of RBC membrane flickering in quantitative phase imaging

Minsuk Lee, Youngjae Won, Byungjun Park, Seungrag Lee

Author Affiliations +

Proceedings Volume 10074, Quantitative Phase Imaging III; 100741T (2017) https://doi.org/10.1117/12.2253458
Event: SPIE BiOS, 2017, San Francisco, California, United States

Abstract

Not only static characteristics but also dynamic characteristics of the red blood cell (RBC) contains useful information for the blood diagnosis. Quantitative phase imaging (QPI) can capture sample images with subnanometer scale depth resolution and millisecond scale temporal resolution. Various researches have been used QPI for the RBC diagnosis, and recently many researches has been developed to decrease the process time of RBC information extraction using QPI by the parallel computing algorithm, however previous studies are interested in the static parameters such as morphology of the cells or simple dynamic parameters such as root mean square (RMS) of the membrane fluctuations. Previously, we presented a practical blood test method using the time series correlation analysis of RBC membrane flickering with QPI. However, this method has shown that there is a limit to the clinical application because of the long computation time. In this study, we present an accelerated time series correlation analysis of RBC membrane flickering using the parallel computing algorithm. This method showed consistent fractal scaling exponent results of the surrounding medium and the normal RBC with our previous research.

Citation Download Citation

Minsuk Lee, Youngjae Won, Byungjun Park, and Seungrag Lee "The study on the parallel processing based time series correlation analysis of RBC membrane flickering in quantitative phase imaging", Proc. SPIE 10074, Quantitative Phase Imaging III, 100741T (21 February 2017); https://doi.org/10.1117/12.2253458

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