Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria

• Published in: Pattern recognition Vol. 40; no. 7; pp. 1899 - 1910
• Main Authors: Castañón, César A.B., Fraga, Jane S., Fernandez, Sandra, Gruber, Arthur, da F. Costa, Luciano
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2007; Elsevier Science
• Subjects: Applied sciences; Avian coccidiosis; Biological and medical sciences; Computerized, statistical medical data processing and models in biomedicine; Eimeria; Exact sciences and technology; Feature extraction; Image processing; Information, signal and communications theory; Medical management aid. Diagnosis aid; Medical sciences; Miscellaneous; Pattern classification; Pattern recognition; Real-time systems; Remote diagnosis; Shape analysis; Signal processing; Telecommunications and information theory; Shape analysis; Image processing; Eimeria; Pattern classification; Feature extraction; Real-time systems; Avian coccidiosis; Remote diagnosis; Automatic classification; Image recognition; Gaussian distribution; Shape detection; Implementation; Learning; Database; Pattern analysis; Digital image; Pattern recognition; Signal classification; Real time system; Discrimination; Signal processing; Bayes methods; Diagnostic aid; Automatic recognition
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2006.12.006

We describe an approach of automatic feature extraction for shape characterization of seven distinct species of Eimeria, a protozoan parasite of domestic fowl. We used digital images of oocysts, a round-shaped stage presenting inter-specific variability. Three groups of features were used: curvature characterization, size and symmetry, and internal structure quantification. Species discrimination was performed with a Bayesian classifier using Gaussian distribution. A database comprising 3891 micrographs was constructed and samples of each species were employed for the training process. The classifier presented an overall correct classification of 85.75%. Finally, we implemented a real-time diagnostic tool through a web interface, providing a remote diagnosis front-end.