Detection of sodium oxalate needles in optical images using neural network classifiers

A description is given of a PC based system for the automatic detection, counting and sizing of sodium oxalate needles in optical microscope images predominated by a background of hydrate particles. The system is primarily based on a neural network classifier which is fed by a feature vector derived from greyscale dynamically thresholded binary images. A backpropagation neural network (BPN) was adopted for technical reasons but any of the other neural network classifiers could have been used. Comparative results are given for the backpropagation, probabilistic (PNN), general regression (GRNN) neural networks and a Gaussian model which show the utility and validity of the neural network approach.