Fluorescent image classification by major color histograms and a neural network | Zendy

Maricor Soriano | Zendy; Luz García | Zendy; Caesar Saloma | Zendy

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Fluorescent image classification by major color histograms and a neural network

Author(s) -

Maricor Soriano,

Luz García,

Caesar Saloma

Publication year - 2001

Publication title -

optics express

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.394

H-Index - 271

ISSN - 1094-4087

DOI - 10.1364/oe.8.000271

Subject(s) - artificial intelligence , pattern recognition (psychology) , histogram , computer science , artificial neural network , feature (linguistics) , classifier (uml) , fluorescence , color histogram , backpropagation , computer vision , image processing , color image , image (mathematics) , optics , physics , philosophy , linguistics

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.

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