Computer-assisted microscopy and multivariate statistics were used to establish and evaluate a procedure for identification of bovine strongylid eggs. Ostertagia ostertagi, Cooperia oncophora, Haemonchus placei, Trichostrongylus axei, and Oesophagostomum radiatum eggs were obtained from faeces voided by monospecifically infected calves. Images of single eggs (400x magnification) were recorded by a CCD camera fitted onto a microscope and digitizied on a PC. After separation of eggs from the image background, the pixel (picture element) positions of the egg outline were analysed by algorithms to describe size and shape. A stepwise discriminant analysis was subsequently used to select and rank descriptive features of 4207 eggs according to discriminatory power. Classification criteria were developed by linear discrimination analysis on the basis of selected features, and the criteria evaluated by cross-validation. A maximum average percentage of correct classification of 85.8% resulted when nineteen features were employed in a linear classification criterion. The percentages correct classification for each species were: O. ostertagi 76.3%, C. oncophora 90.8%, O. radiatum 87.8%, H. placei 90.1%, and T. axei 83.8%. Classification based on the five most important features gave an overall correct classification of 81.5%. Images of 'unknown' eggs could be identified automatically by the classification criteria after procedural steps performed by PC were linked in a batch program.
|Journal||Journal of Helminthology|
|Publication status||Published - 1996|