Tissue Characterization by Information-theoretic Entropy of Ultrasound Signal

The conventional ultrasound gray scale image the so-called B-mode image (Brightness-mode), has been widely applied in the clinical medicine. Its primary purpose is to qualitatively present the structure, shape and contour of the biological tissue. However, the brightness of the B-mode image is would be affected by many factors, such as system gain, dynamic range, operator’s experience and etc. Besides, in order to avoid the speckle effect on the image quality, the weaker backscattering signal is typically removed in the existing medical ultrasound systems. Note that ultrasound backscattering signal is related to the properties of scatterers in tissues, such as, size, shape, density and concentration. Therefore, the filtering of scattering signal makes the B-mode image difficult to provide the quantitative information of scatterers, which in turn influences the early detection and classification on benign and malignant tissues.ased upon the fact that the essence of ultrasound backscattering signal belongs to random signals, many researchers explored using statistical models to describe the probability density function of backscattering echoes to complement the deficiency of B-scan. The statistical models mainly include Rayleigh, K, homodyned K, generalized K, and Nakagami, in which Nakagami statistical model can encompass all scattering conditions in ultrasound. But, under certain circumstances, the backscattered statistics do not obey the Nakagami distribution anymore once there are some nonlinear effects or processing on the backscattering signals. This limits the practical applications of statistical models.n order to solve the problem, this study proposed using information-theoretic entropy of ultrasonic backscattering signal to quantify the properties of tissue. The superiority of information-theoretic entropy lies in that it can reflect the scatterer properties without any limitation due to statistical models on the backscattering echoes. To explore the idea, we carried out experiments on phantoms. First of all, we set up ultrasound image scanning system, which holds the ultrasonic transducer with different frequencies for image scanning. Subsequently, we made phantoms with different scattering concentrations and deal with data acquisition and gray image formation. Meanwhile, we use the envelope signals to calculate three information-theoretic entropies (i.e. Shannon, Renyi, Tsallis.) to explore the entropies as a function of scatterer concentrations. The results between using Nakagami models and entropies will be compared and discuss the effects of ultrasonic frequencies and background speckles on the performance of entropy to characterize tissues. he show the Nakagami parameter has a better dynamic range to detect the variation of scatterer concentration when low frequency focused transducer was used. With the increase in frequency, the dynamic range decreased in the same range of scatterer concentration as frequency increases. The main reason is that the probability distribution of signal will be close to pre-Rayleigh distribution with increasing the ultrasound frequency. Tsallis entropy has an outstanding performance to quantify the scatterer concentration. Besides a high dynamic range, its relationship with scatterer concentration would become more linear by increasing ultrasonic frequency. Meanwhile, under the influence of background speckle effect, it also can be applied to tissues with higher scatterer concentration.