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Overview of automated sickle cell disease diagnosis by analysis of spatio-temporal cell dynamics in digital holographic microscopy
Author(s): Timothy O'Connor; Bahram Javidi; Adam Markman; Arun Anand; Biree Andemariam
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Paper 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.

Paper Details

Date Published: 14 May 2019
PDF: 6 pages
Proc. SPIE 10997, Three-Dimensional Imaging, Visualization, and Display 2019, 109970S (14 May 2019); doi: 10.1117/12.2521150
Show Author Affiliations
Timothy O'Connor, Univ. of Connecticut (United States)
Bahram Javidi, Univ. of Connecticut (United States)
Adam Markman, Univ. of Connecticut (United States)
Arun Anand, M. S. Univ. of Baroda (India)
Biree Andemariam, Univ. of Connecticut Health Ctr. (United States)


Published in SPIE Proceedings Vol. 10997:
Three-Dimensional Imaging, Visualization, and Display 2019
Bahram Javidi; Jung-Young Son; Osamu Matoba, Editor(s)

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