A Fuzzy Inference System for Identifying Tissue Elasticity Using Ultrasound

Abstract

This paper proposes a fuzzy rule-based approach for identifying tissue elasticity using ultrasound. The purpose of this paper identifies automatically tissue elasticity. Information of tissue elasticity helps us to diagnose several diseases. Elastography was able to estimate tissue elasticity. However, this measurement range is limited due to the need of pressure. To avoid this limitation, this paper proposes the identification system without pressure. This inference system consists of two stages. In the first stage, fuzzy membership functions are constructed by known data of elasticity. The second stage identifies elasticity of unknown data by using the membership functions. We used five different phantoms (total 5×10 = 50) of elasticity as known data and applied this system into nine different phantoms (total 9×10 = 90) of elasticity as unknown data. As a result, the correlation coefficient between actual value and identified value was 0.789 and the error of means was 0.646. This system thus acquired smaller error ratio than that of the statistical method.