Learning image transformations via classification of micro-regions
| Grant number: | 14/05431-2 |
| Support type: | Regular Research Grants - Publications - Scientific article |
| Duration: | May 01, 2014 - October 31, 2014 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science |
| Principal Investigator: | João Paulo Papa |
| Grantee: | João Paulo Papa |
| Home Institution: | Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil |
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)