Classification of Human Parasite Eggs based on Enhanced Multitexton Histogram

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Flores-Quispe, R
Velazco-Paredes, Y
Escarcina, REP
Castanon, CAB
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IEEE Computer Society
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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.
Our acknowledgment to San Pablo Catholic University for sponsoring the publication of this article and support the development of this work and the Presidency of the Council of Ministers of Peru through the Science and Technology Fund (FINCyT) and Council for Science, Technology and Innovation of Peru (CONCYTEC) for support of this work through the doctoral fellowships for Roxana Flores and Yuber Velazco".
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Multitexton Histogram descriptor, CBIR, Human Parasite Eggs