Automatic Segmentation and Classification of Human Intestinal Parasites from Microscopy Images

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Suzuki:2012:ieeeTBME,
  author =       "Celso T. N. Suzuki and Jancarlo F. Gomes and 
                 Alexandre X. Falcao and Joao P. Papa and Sumie Hoshino-Shimizu",
  title =        "Automatic Segmentation and Classification of Human
                 Intestinal Parasites from Microscopy Images",
  journal =      "IEEE Transactions on Biomedical Engineering",
  year =         "2013",
  volume =       "60",
  number =       "3",
  pages =        "803--812",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, image
                 foresting transform (IFT), image segmentation,
                 intestinal parasitosis, microscopy image analysis,
                 optimum-path forest (OPF) classifier, pattern
                 recognition.",
  DOI =          "doi:10.1109/TBME.2012.2187204",
  ISSN =         "0018-9294",
  size =         "10 pages",
  abstract =     "Human intestinal parasites constitute a problem in
                 most tropical countries, causing death or physical and
                 mental disorders. Their diagnosis usually relies on the
                 visual analysis of microscopy images, with error rates
                 that may range from moderate to high. The problem has
                 been addressed via computational image analysis, but
                 only for a few species and images free of fecal
                 impurities. In routine, fecal impurities are a real
                 challenge for automatic image analysis. We have
                 circumvented this problem by a method that can segment
                 and classify, from bright field microscopy images with
                 fecal impurities, the 15 most common species of
                 protozoan cysts, helminth eggs, and larvae in Brazil.
                 Our approach exploits ellipse matching and image
                 foresting transform for image segmentation, multiple
                 object descriptors and their optimum combination by
                 genetic programming for object representation, and the
                 optimum-path forest classifier for object recognition.
                 The results indicate that our method is a promising
                 approach toward the fully automation of the
                 enteroparasitosis diagnosis.",
  notes =        "Also known as \cite{6146453}",
}

Genetic Programming entries for Celso Tetsuo Nagase Suzuki Jancarlo Ferreira Gomes Alexandre X Falcao Joao Paulo Papa Sumie Hoshino-Shimizu

Citations