Liver tissue characterization for each pixel in ultrasound image using multi-Rayleigh model

In our previous study, we proposed the multi-Rayleigh model as an amplitude distribution model of fibrotic liver, and succeeded in the quantitative evaluation of liver fibrosis in the region of interest. In this paper, to evaluate liver fibrosis more accurately, the amplitude of each pixel in a clinical echo image was converted to hypoechoic and fibrotic probabilities using the multi-Rayleigh model. Clinical echo images of liver fibrosis were analyzed and the relationship between these probabilities and the stage of liver fibrosis were discussed. We also showed that the information on fibrotic tissue can be extracted more accurately using the fibrotic probability than using the conventional method based on the constant false alarm rate (CFAR) processing. We conclude that the proposed method is valid for the quantitative diagnosis of liver fibrosis.