Automatic identification of fungi under complex microscopic fecal images.

Automatic identification of fungi in microscopic fecal images provides important information for evaluating digestive diseases. To date, disease diagnosis is primarily performed by manual techniques. However, the accuracy of this approach depends on the operator's expertise and subjective factors. The proposed system automatically identifies fungi in microscopic fecal images that contain other cells and impurities under complex environments. We segment images twice to obtain the correct area of interest, and select ten features, including the circle number, concavity point, and other basic features, to filter fungi. An artificial neural network (ANN) system is used to identify the fungi. The first stage (ANN-1) processes features from five images in differing focal lengths; the second stage (ANN-2) identifies the fungi using the ANN-1 output values. Images in differing focal lengths can be used to improve the identification result. The system output accurately detects the image, whether or not it has fungi. If the image does have fungi, the system output counts the number of different fungi types.