Breast and liver cancer detection from ultrasound images using tissue characterization

Georgia Georgiou

doi:10.17918/00000771

This dissertation considers the problem of liver and breast tissue characterization using ultrasound radio-frequency (RF) data. Our main goal is to develop a tissue characterization scheme that is able to detect cancer in liver and breast tissue, and is closely related to the tissue microstructure. One of the most important aspects of this dissertation is the model employed for the tissue structure. Liver and breast tissue is composed of two major kinds of scattering structures, 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 scatterers, the correlation between neighboring unresolvable scatterers in the tissue, etc. Empirical ROC 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. With this work we try to expand the limits of the currently used clinical ultrasonography. It is our belief that ultrasound imaging systems of the future will employ a quantitative tissue characterization method that will assist the radiologist by extracting structural tissue information not seen on the B-scan image under examination.

Breast and liver cancer detection from ultrasound images using tissue characterization

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Breast and liver cancer detection from ultrasound images using tissue characterization

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