A probability model for analyzing speckles in intravascular ultrasound images to facilitate image segmentation / 南方医科大学学报

ABSTRACT

Ultrasonic image speckles result from the interference of the reflected signals by the scatters in the detected tissue. The physical characteristics of the speckles are closely correlated with the structures of the biological tissues, and the probability distribution of these speckles differs across different tissues. Based on the probability characteristics of intravascular ultrasound (IVUS) speckles, a Gamma mixture model and Gaussian mixture model are proposed to describe the calcified plaque, soft plaque and normal vascular regions on IVUS images. Using KS test, KL divergence and correlation coefficient analysis, we found that the probability distributions of the speckles generated by calcified plaques and normal blood vessels were better described by the Gaussian mixture model, while the speckles caused by soft plaques were described better by the Gamma mixture model. Based on this finding, we propose a probability mixture model combining neighborhood information for plaque segmentation on IVUS images. Compared with the existing probabilistic mixture model, the segmentation accuracy was greatly improved with a reduced noise.