Classification of human parasite eggs based on enhanced multitexton histogram

The Content-based image retrieval (CBIR) systems and their application in different areas of development, are current research topics, for that reason in this study content-based image retrieval is applied to classificate eight different human parasite eggs: Ascarias, Uncinarias, Trichuris, Dyphillobothrium-Pacificum, Taenia-Solium, Fasciola Hepática and Enterobius-Vermicularis, which are into the class of Helminthes, from their microscopic images. This proposed system includes two stages. In first stage, a feature extraction mechanism that is based on multitexton histogram descriptor (MTH) which has been improved and called `Enhanced MTH´. In second stage, an CBIR system has been implemented in orden to classificate the differents microscopic images to identify their correct species. Finally, simulation result shows overall success rates of 92,16% in the classification.