Automatic recognition of biological particles in microscopic images

  • Authors:

  • M. Ranzato;P. E. Taylor;J. M. House;R. C. Flagan;Y. LeCun;P. Perona

  • Affiliations:

  • The Courant Institute, New York University, 719, Broadway, 12th fl., New York, NY 10003, USA;California Institute of Technology, 136-93, Pasadena, CA 91125, USA;California Institute of Technology, 136-93, Pasadena, CA 91125, USA;California Institute of Technology, 136-93, Pasadena, CA 91125, USA;The Courant Institute, New York University, 719, Broadway, 12th fl., New York, NY 10003, USA;California Institute of Technology, 136-93, Pasadena, CA 91125, USA

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2007

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Abstract

A simple and general-purpose system to recognize biological particles is presented. It is composed of four stages: First (if necessary) promising locations in the image are detected and small regions containing interesting samples are extracted using a feature finder. Second, differential invariants of the brightness are computed at multiple scales of resolution. Third, after point-wise non-linear mappings to a higher dimensional feature space, this information is averaged over the whole region thus producing a vector of features for each sample that is invariant with respect to rotation and translation. Fourth, each sample is classified using a classifier obtained from a mixture-of-Gaussians generative model. This system was developed to classify 12 categories of particles found in human urine; it achieves a 93.2% correct classification rate in this application. It was subsequently trained and tested on a challenging set of images of airborne pollen grains where it achieved an 83% correct classification rate for the three categories found during one month of observation. Pollen classification is challenging even for human experts and this performance is considered good.