Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network.

In order to automate routine fecal examination for parasitic diseases, we propose in this study a computer processing algorithm using digital image processing techniques and an artificial neural network (ANN) classifier. The morphometric characteristics of eggs of human parasites in fecal specimens were extracted from microscopic images through digital image processing. An ANN then identified the parasite species based on those characteristics. We selected four morphometric features based on three morphological characteristics representing shape, shell smoothness, and size. A total of 82 microscopic images containing seven common human helminth eggs were used. The first stage (ANN-1) of the proposed ANN classification system isolated eggs from confusing artifacts. The second stage (ANN-2) classified eggs by species. The performance of ANN was evaluated by the tenfold cross-validation method to obviate the dependency on the selection of training samples. Cross-validation results showed 86.1% average correct classification ratio for ANN-1 and 90.3% for ANN-2 with small variances of 46.0 and 39.0, respectively. The algorithm developed will be an essential part of a completely automated fecal examination system.