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 p ropose in this study a computer processing algorithm using digital image pr ocessing 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. W e selected four morphometric features based on three morphological characte ristics representing shape, shell smoothness, and size, A total of 82 micro scopic images containing seven common human helminth eggs were used, The fi rst stage (ANN-1) of the proposed ANN classification system isolated eggs f rom confusing artifacts. The second stage (ANN-2) classified eggs by specie s, The performance of ANN was evaluated by the tenfold cross-validation met hod to obviate the dependency on the selection of training samples. Cross-v alidation 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 automate d fecal examination system.