Recursive autoregressive spectral maps for ocular pathology detection.

Abstract

This article presents a new approach to the problem of obtaining topological maps for tissue characterization, based on spectral parameters extracted from radio frequency (RF) backscattered ultrasonic signals. The spectral parameter we deal with is the power spectral density centroid, since it is an efficient indicator of the tissue microstructure characteristics as far as the particle dimensions are concerned. The spectral analysis of RF ultrasonic echoes is performed using a recursive least-squares scheme with a variable forgetting factor, based on low-order autoregressive models. The proposed technique is particularly tailored to the differentiation of ocular pathologies; moreover, it is capable of tracking the spatial high-varying signal characteristics. The proposed approach was tested on simulated signals and on a gel suspension of calibrated latex spheres; finally, it was applied to signals scattered by in vitro eye specimens, giving satisfactory results in terms of frequency resolution and computational efficiency. The reduced computational burden allows an on-line implementation of the procedure. Topological spectral maps, combined with the conventional B-mode display, may offer a complete and integrated diagnostic tool, able to locally characterize the investigated tissue region in terms of amplitude and frequency shift of the corresponding echoes.