Classification of irregularly shaped micro-objects using complex Fourier descriptors

A new method for characterizing complex irregular shapes of natural objects (e.g. plant cells, aerosol particles) is presented. The method employs complex Fourier analysis rather than the traditional forms of the Fourier analysis. Digitized images of objects are processed and the contours are tracked by a classical boundary following technique. A new contour preprocessing technique allows to normalize position, size and orientation of a contour. A new contour resampling technique results in a more precise polygonal approximation of the contour. The resampled contour is represented in a complex plane and its complex Fourier coefficients are computed. The technique is applied to the classification of individual algae cells and their agglomerates employing two different classification methods: hierarchical clustering and neural network. The results demonstrate the applicability of the method for the classification of complex irregular shapes.