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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Automatic Segmentation and Classification of Human Intestinal Parasites From Microscopy Images

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Author(s):
Suzuki, Celso T. N. [1] ; Gomes, Jancarlo F. [1] ; Falcao, Alexandre X. [1] ; Papa, Joao P. [2] ; Hoshino-Shimizu, Sumie [3]
Total Authors: 5
Affiliation:
[1] Univ Estadual Campinas, Inst Comp, BR-13084971 Sao Paulo - Brazil
[2] Univ Estadual Paulista, Dept Comp Sci, BR-05508900 Sao Paulo - Brazil
[3] Univ Sao Paulo, Fac Pharmaceut Sci, BR-66318 Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: IEEE Transactions on Biomedical Engineering; v. 60, n. 3, p. 803-812, MAR 2013.
Web of Science Citations: 24
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. (AU)

FAPESP's process: 09/16206-1 - New trends on optimum-path forest-based pattern recognition
Grantee:João Paulo Papa
Support type: Research Grants - Young Investigators Grants