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Journal of the Optical Society of America A

Journal of the Optical Society of America A


  • Editor: Franco Gori
  • Vol. 28, Iss. 6 — Jun. 1, 2011
  • pp: 1204–1210

Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis

Ran Liu, Dipak K. Dey, Daniel Boss, Pierre Marquet, and Bahram Javidi  »View Author Affiliations

JOSA A, Vol. 28, Issue 6, pp. 1204-1210 (2011)

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We propose to apply statistical clustering algorithms on a three-dimensional profile of red blood cells (RBCs) obtained through digital holographic microscopy (DHM). We show that two classes of RBCs stored for 14 and 38 days can be effectively classified. Two-dimensional intensity images of these cells are virtually the same. DHM allows for measurement of the RBCs’ biconcave profile, resulting in a discriminative dataset. Two statistical clustering algorithms are compared. A model-based clustering approach classifies the pixels of an RBC and recognizes the RBC as either new or old based. The K-means algorithm is applied to the four-dimensional feature vector extracted from the RBC profile.

© 2011 Optical Society of America

OCIS Codes
(150.0150) Machine vision : Machine vision
(150.6910) Machine vision : Three-dimensional sensing
(150.1135) Machine vision : Algorithms
(090.1995) Holography : Digital holography

ToC Category:
Machine Vision

Original Manuscript: January 10, 2011
Revised Manuscript: March 29, 2011
Manuscript Accepted: April 19, 2011
Published: May 24, 2011

Virtual Issues
Vol. 6, Iss. 7 Virtual Journal for Biomedical Optics

Ran Liu, Dipak K. Dey, Daniel Boss, Pierre Marquet, and Bahram Javidi, "Recognition and classification of red blood cells using digital holographic microscopy and data clustering with discriminant analysis," J. Opt. Soc. Am. A 28, 1204-1210 (2011)

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