Neural networks and higher order spectra for breast cancer detection

The research work contained in this paper is concerned with the use of higher order spectral estimation techniques for the derivation of the parameters of two dimensional autoregressive (AR) models. The specific application of the developed method is in mammography, an area in which it is very difficult to discern the appropriate features. The required segmentation of such 2-D random fields is effected through the additional stage of a neural network having as inputs the extracted autoregressive parameters. The results show significant discriminating gains through such techniques. The directionality of the cumulant space has been observed to influence the AR parameter estimation and this forms another area for examination