Effects of hypertonic solutions on two species of human intestinal parasites during fecal examination.

Aim: Ova and parasite examination by flotation requires hypertonic solutions, which can damage the egg and cyst membranes, leading to false negatives. The authors investigated the harmful effects of ZnSO4 and C12H22O11 solutions on the ova and parasite examination. Materials & methods: The authors processed samples using the Three Fecal Test technique. Aliquots were floated in different pH levels, temperatures and solution densities. Results: Densities above 1.12 g/ml led structures to collapse after 6-10 min. pH neutralization of the ZnSO4 solution did not prevent the parasites from changing. Conclusion: All structures were altered when standard methods were performed. To delay collapse, the parasite floating under 5 °C is highly desirable.

Fecal exams require solutions that can damage the intestinal parasite's shape. This is bad for diagnosis. The authors investigated the harmful effects of these solutions on fecal exams. The authors processed samples using a technique called the Three Fecal Test. Fecal samples were floated in different conditions, including neutral and acidic solutions, high and low temperatures and varying densities of chemical solutions. Densities above 1.12 g/ml altered the structures of parasites. Neutral solutions did not prevent the structures from changing. The structures of all parasites were altered when the usual techniques were performed. Thus, the techniques for diagnosing intestinal parasites in feces must be improved. Temperatures under 5 °C are the best for preventing the destruction of parasite membranes.

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.