Automatic segmentation and classification of human intestinal parasites from microscopy images.
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.
Institute of Computing, University of Campinas, São Paulo 13084-971, Brazil. jgomes@ic.unicamp.br
Celso T N
CT
Suzuki
Jancarlo F
JF
Gomes
Alexandre X
AX
Falcão
João P
JP
Papa
Sumie
S
Hoshino-Shimizu
D000818:Animals; D005243:Feces / Q000469:parasitology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007411:Intestinal Diseases, Parasitic / Q000175:diagnosis / Q000469:parasitology; D008853:Microscopy; D010271:Parasites / Q000033:anatomy & histology / Q000145:classification*; D010363:Pattern Recognition, Automated / Q000379:methods*