Robust autoregressive modelling through higher order spectral estimation techniques with applications to mammography

The research work reported in this paper is concerned with the use of higher order spectral estimation techniques as a means to deriving the parameters of two dimensional autoregressive (AR) models. Image analysis is examined front a higher order statistical perspective and in the context of noise. The objective is to develop analysis techniques through which robust autoregressive parameter estimation is accomplished. The approach taken involves the use of 2-D AR models derived from third order cumulants. The directivity of the cumulant space influences the AR parameter estimation in a decisive manner. The specific application of the developed methods is in mammography, an area in which it is very difficult to discern the appropriate features. The results show significant discriminating gains through such techniques