Particle shape characterization using image analysis and neural networks

An image analysis method is presented which effectively describes the shape of a convex or concave particle. The method uses the Fourier descriptors evaluated from the Fourier series expansion of the angular bend of the periphery of a particle as a function of its are length. The Fourier descriptors are then used as inputs to unsupervised or supervised artificial neural networks to cluster and classify particles according to their shape. A number describing the class of a single particle or the average class of a population of particles can therefore be deduced to characterize them.