The analysis and classification of small-scale tissue structures using the generalized spectrum

Abstract

Conventional ultrasonic imaging systems primarily use backscattered signals for creating qualitative images that reveal large-scale structures, such as tissue boundaries. Efforts to extract additional quantitative information have resulted in limited success. The nonstationarities of the scatterers comprising biological tissue often violate conditions for applying common signal characterization and estimation methods. In addition, ultrasonic tissue properties (such as attenuation, velocity, scatterer density, size, and structure) ambiguously encode information into the backscattered signal, making it difficult or impossible to extract and quantify a single property. This paper presents the generalized spectrum (GS) as a method for analyzing and quantifying the properties of small-scale resolvable structures (on the order of 1 to 4 mm), which result from tissue structures such as lobules, ducts, and vessels. The GS extends the capabilities of power spectral density to include meaningful phase information that results from small-scale structure. The relevant properties of the GS include its ability to reduce the effects’ diffuse scatterers (speckle), and permit normalization schemes that significantly limit the effects of system response and attenuation from the overlying tissue. An implementation of the GS in an analysis and classification of normal and metastic liver tissues is also described.