Overview of automated sickle cell disease diagnosis by analysis of spatio-temporal cell dynamics in digital holographic microscopy

Timothy O'Connor; Bahram Javidi; Adam Markman; Arun Anand; Biree Andemariam

doi:10.1117/12.2521150

14 May 2019 Overview of automated sickle cell disease diagnosis by analysis of spatio-temporal cell dynamics in digital holographic microscopy

Timothy O'Connor, Bahram Javidi, Adam Markman, Arun Anand, Biree Andemariam

Author Affiliations +

Proceedings Volume 10997, Three-Dimensional Imaging, Visualization, and Display 2019; 109970S (2019) https://doi.org/10.1117/12.2521150
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

We overview a previously reported system for automated diagnosis of sickle cell disease based on red blood cell (RBC) membrane fluctuations measured via digital holographic microscopy. A low-cost, compact, 3D-printed shearing interferometer is used to record video holograms of RBCs. Each hologram frame is reconstructed in order to form a spatio-temporal data cube from which features regarding membrane fluctuations are extracted. The motility-based features are combined with static morphology-based cell features and inputted into a random forest classifier which outputs the disease state of the cell with high accuracy.

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Timothy O'Connor, Bahram Javidi, Adam Markman, Arun Anand, and Biree Andemariam "Overview of automated sickle cell disease diagnosis by analysis of spatio-temporal cell dynamics in digital holographic microscopy", Proc. SPIE 10997, Three-Dimensional Imaging, Visualization, and Display 2019, 109970S (14 May 2019); https://doi.org/10.1117/12.2521150

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KEYWORDS

Digital holography

Blood

Holography

Microscopy

Video

Holograms

Feature extraction

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