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Optics Express

Optics Express

  • Editor: J. H. Eberly
  • Vol. 8, Iss. 5 — Feb. 26, 2001
  • pp: 271–277

Fluorescent image classification by major color histograms and a neural network

M. Soriano, L. Garcia, and C. Saloma  »View Author Affiliations

Optics Express, Vol. 8, Issue 5, pp. 271-277 (2001)

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Efficient image classification of microscopic fluorescent spheres is demonstrated with a supervised backpropagation neural network (NN) that uses as inputs the major color histogram representation of the fluorescent image to be classified. Two techniques are tested for the major color search: (1) cluster mean (CM) and (2) Kohonen’s self-organizing feature map (SOFM). The method is shown to have higher recognition rates than Swain and Ballard’s Color Indexing by histogram intersection. Classification with SOFM-generated histograms as inputs to the classifier NN achieved the best recognition rate (90%) for cases of normal, scaled, defocused, photobleached, and combined images of AMCA (7-Amino-4-Methylcoumarin-3-Acetic Acid) and FITC (Fluorescein Isothiocynate)-stained microspheres.

© Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(330.1880) Vision, color, and visual optics : Detection
(330.5000) Vision, color, and visual optics : Vision - patterns and recognition

ToC Category:
Research Papers

Original Manuscript: January 18, 2001
Published: February 26, 2001

M. Soriano, L. Garcia, and Caesar Saloma, "Fluorescent image classification by major color histograms and a neural network," Opt. Express 8, 271-277 (2001)

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