Is early detection of liver and breast cancers from ultrasound scans possible?

• Authors: G. Georgiou;F. S. Cohen
• Venue: Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
• Year: 2003

This paper presents an integral approach for the tissue characterization problem. Such an approach includes a model, estimation algorithms and an evaluation method. This work focuses on liver and breast tissue characterization but it may be applicable to other tissue types after proper modifications. Liver and breast tissue is composed of two major kinds of scattering structure, i.e., the liver and breast parenchyma, which is relatively large and thus resolvable using the current ultrasonic transducers, and liver and breast cells which are not resolvable. In this work, we propose a decomposition approach for the RF echo into two components, namely the coherent and diffuse component, which are related to the resolvable and unresolvable scatterers in the liver and breast structure, respectively. Structural differences between the liver and breast, related to the resolvable scatterers properties, led us to develop two different decomposition algorithms. The first algorithm was developed for the liver RF echo and was based on the quasi-periodic structure of the liver lobules. Breast tissue decomposition was based on a more general model for the resolvable scatterers echo, because the breast tissue parenchyma is far from regular. By using the proposed decomposition we were able to estimate structural parameters of the liver and breast such as the average spacing of the liver lobules, the energy of the resolvable and unresolvable scatterers, and the correlation between neighboring unresolvable scatterers in the tissue. Empirical receiver operating characteristics analysis was applied to the parameters estimated from a large database of liver and breast B-scan images, to evaluate their diagnostic power. Single parameters of the liver and breast tissue showed good discriminating power between cancerous and normal liver and breast tissue, and also between malignant and benign breast tissue. The ability to identify small breast lesions (4 mm) is also demonstrated.