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In this study, an ultrasound Radio-Frequency (RF) data of healthy and tumour breast regions are considered for tissue characterisation. The main aim of this study is to differentiate the consistent statistical distribution of backscattered RF data with the objective of performing semi-automatic segmentation based on statistics. The differentiation considers Gamma, Generalised Extreme Value (GEV), Log-normal, and Rayleigh distributions. The accuracy of the statistical parameters is measured based on the criteria of both the Kolmogorov - Smirnov Test  ( KS) and Mean Square Error (MSE) goodness of fit. Results show that there is a possibility of using the parameters of Rayleigh, Gamma, and GEV for different healthy and tumour tissue regions, where GEV yields the best goodness of fit test and its parameters have a good potential to be exploited for further study for segmentation purposes.

Breast Tissue Characterization Based on Generalised Extreme Value Distribution of Ultrasound Radio-Frequency Data